Alex Waibel

Also published as: A. Waibel, Alexander Waibel


2021

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Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Text-based Translation
Zhong Zhou | Alexander Waibel
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP

We translate a closed text that is known in advance into a severely low resource language by leveraging massive source parallelism. In other words, given a text in 124 source languages, we translate it into a severely low resource language using only ∼1,000 lines of low resource data without any external help. Firstly, we propose a systematic method to rank and choose source languages that are close to the low resource language. We call the linguistic definition of language family Family of Origin (FAMO), and we call the empirical definition of higher-ranked languages using our metrics Family of Choice (FAMC). Secondly, we build an Iteratively Pretrained Multilingual Order-preserving Lexiconized Transformer (IPML) to train on ∼1,000 lines (∼3.5%) of low resource data. In order to translate named entities well, we build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages. Moreover, we also build a novel method of combining translations from different source languages into one. Using English as a hypothetical low resource language, we get a +23.9 BLEU increase over a multilingual baseline, and a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset. We get a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset. We also have good results for a real severely low resource Mayan language, Eastern Pokomchi.

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Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages
Zhong Zhou | Alex Waibel
Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)

We translate a closed text that is known in advance and available in many languages into a new and severely low resource language. Most human translation efforts adopt a portionbased approach to translate consecutive pages/chapters in order, which may not suit machine translation. We compare the portion-based approach that optimizes coherence of the text locally with the random sampling approach that increases coverage of the text globally. Our results show that the random sampling approach performs better. When training on a seed corpus of ∼1,000 lines from the Bible and testing on the rest of the Bible (∼30,000 lines), random sampling gives a performance gain of +11.0 BLEU using English as a simulated low resource language, and +4.9 BLEU using Eastern Pokomchi, a Mayan language. Furthermore, we compare three ways of updating machine translation models with increasing amount of human post-edited data through iterations. We find that adding newly post-edited data to training after vocabulary update without self-supervision performs the best. We propose an algorithm for human and machine to work together seamlessly to translate a closed text into a severely low resource language.

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Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)
Marcello Federico | Alex Waibel | Marta R. Costa-jussà | Jan Niehues | Sebastian Stuker | Elizabeth Salesky
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

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FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN
Antonios Anastasopoulos | Ondřej Bojar | Jacob Bremerman | Roldano Cattoni | Maha Elbayad | Marcello Federico | Xutai Ma | Satoshi Nakamura | Matteo Negri | Jan Niehues | Juan Pino | Elizabeth Salesky | Sebastian Stüker | Katsuhito Sudoh | Marco Turchi | Alexander Waibel | Changhan Wang | Matthew Wiesner
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation. A total of 22 teams participated in at least one of the tasks. This paper describes each shared task, data and evaluation metrics, and reports results of the received submissions.

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Multilingual Speech Translation KIT @ IWSLT2021
Ngoc-Quan Pham | Tuan Nam Nguyen | Thanh-Le Ha | Sebastian Stüker | Alexander Waibel | Dan He
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

This paper contains the description for the submission of Karlsruhe Institute of Technology (KIT) for the multilingual TEDx translation task in the IWSLT 2021 evaluation campaign. Our main approach is to develop both cascade and end-to-end systems and eventually combine them together to achieve the best possible results for this extremely low-resource setting. The report also confirms certain consistent architectural improvement added to the Transformer architecture, for all tasks: translation, transcription and speech translation.

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ELITR Multilingual Live Subtitling: Demo and Strategy
Ondřej Bojar | Dominik Macháček | Sangeet Sagar | Otakar Smrž | Jonáš Kratochvíl | Peter Polák | Ebrahim Ansari | Mohammad Mahmoudi | Rishu Kumar | Dario Franceschini | Chiara Canton | Ivan Simonini | Thai-Son Nguyen | Felix Schneider | Sebastian Stüker | Alex Waibel | Barry Haddow | Rico Sennrich | Philip Williams
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

This paper presents an automatic speech translation system aimed at live subtitling of conference presentations. We describe the overall architecture and key processing components. More importantly, we explain our strategy for building a complex system for end-users from numerous individual components, each of which has been tested only in laboratory conditions. The system is a working prototype that is routinely tested in recognizing English, Czech, and German speech and presenting it translated simultaneously into 42 target languages.

2020

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DaCToR: A Data Collection Tool for the RELATER Project
Juan Hussain | Oussama Zenkri | Sebastian Stüker | Alex Waibel
Proceedings of the 12th Language Resources and Evaluation Conference

Collecting domain-specific data for under-resourced languages, e.g., dialects of languages, can be very expensive, potentially financially prohibitive and taking long time. Moreover, in the case of rarely written languages, the normalization of non-canonical transcription might be another time consuming but necessary task. In order to collect domain-specific data in such circumstances in a time and cost-efficient way, collecting read data of pre-prepared texts is often a viable option. In order to collect data in the domain of psychiatric diagnosis in Arabic dialects for the project RELATER, we have prepared the data collection tool DaCToR for collecting read texts by speakers in the respective countries and districts in which the dialects are spoken. In this paper we describe our tool, its purpose within the project RELATER and the dialects which we have started to collect with the tool.

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Proceedings of the 17th International Conference on Spoken Language Translation
Marcello Federico | Alex Waibel | Kevin Knight | Satoshi Nakamura | Hermann Ney | Jan Niehues | Sebastian Stüker | Dekai Wu | Joseph Mariani | Francois Yvon
Proceedings of the 17th International Conference on Spoken Language Translation

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FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN
Ebrahim Ansari | Amittai Axelrod | Nguyen Bach | Ondřej Bojar | Roldano Cattoni | Fahim Dalvi | Nadir Durrani | Marcello Federico | Christian Federmann | Jiatao Gu | Fei Huang | Kevin Knight | Xutai Ma | Ajay Nagesh | Matteo Negri | Jan Niehues | Juan Pino | Elizabeth Salesky | Xing Shi | Sebastian Stüker | Marco Turchi | Alexander Waibel | Changhan Wang
Proceedings of the 17th International Conference on Spoken Language Translation

The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation. A total of teams participated in at least one of the tracks. This paper introduces each track’s goal, data and evaluation metrics, and reports the results of the received submissions.

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KIT’s IWSLT 2020 SLT Translation System
Ngoc-Quan Pham | Felix Schneider | Tuan-Nam Nguyen | Thanh-Le Ha | Thai Son Nguyen | Maximilian Awiszus | Sebastian Stüker | Alexander Waibel
Proceedings of the 17th International Conference on Spoken Language Translation

This paper describes KIT’s submissions to the IWSLT2020 Speech Translation evaluation campaign. We first participate in the simultaneous translation task, in which our simultaneous models are Transformer based and can be efficiently trained to obtain low latency with minimized compromise in quality. On the offline speech translation task, we applied our new Speech Transformer architecture to end-to-end speech translation. The obtained model can provide translation quality which is competitive to a complicated cascade. The latter still has the upper hand, thanks to the ability to transparently access to the transcription, and resegment the inputs to avoid fragmentation.

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Towards Stream Translation: Adaptive Computation Time for Simultaneous Machine Translation
Felix Schneider | Alexander Waibel
Proceedings of the 17th International Conference on Spoken Language Translation

Simultaneous machine translation systems rely on a policy to schedule read and write operations in order to begin translating a source sentence before it is complete. In this paper, we demonstrate the use of Adaptive Computation Time (ACT) as an adaptive, learned policy for simultaneous machine translation using the transformer model and as a more numerically stable alternative to Monotonic Infinite Lookback Attention (MILk). We achieve state-of-the-art results in terms of latency-quality tradeoffs. We also propose a method to use our model on unsegmented input, i.e. without sentence boundaries, simulating the condition of translating output from automatic speech recognition. We present first benchmark results on this task.

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Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems
William M. Campbell | Alex Waibel | Dilek Hakkani-Tur | Timothy J. Hazen | Kevin Kilgour | Eunah Cho | Varun Kumar | Hadrien Glaude
Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems

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Supervised Adaptation of Sequence-to-Sequence Speech Recognition Systems using Batch-Weighting
Christian Huber | Juan Hussain | Tuan-Nam Nguyen | Kaihang Song | Sebastian Stüker | Alexander Waibel
Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems

When training speech recognition systems, one often faces the situation that sufficient amounts of training data for the language in question are available but only small amounts of data for the domain in question. This problem is even bigger for end-to-end speech recognition systems that only accept transcribed speech as training data, which is harder and more expensive to obtain than text data. In this paper we present experiments in adapting end-to-end speech recognition systems by a method which is called batch-weighting and which we contrast against regular fine-tuning, i.e., to continue to train existing neural speech recognition models on adaptation data. We perform experiments using theses techniques in adapting to topic, accent and vocabulary, showing that batch-weighting consistently outperforms fine-tuning. In order to show the generalization capabilities of batch-weighting we perform experiments in several languages, i.e., Arabic, English and German. Due to its relatively small computational requirements batch-weighting is a suitable technique for supervised life-long learning during the life-time of a speech recognition system, e.g., from user corrections.

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Incorporating External Annotation to improve Named Entity Translation in NMT
Maciej Modrzejewski | Miriam Exel | Bianka Buschbeck | Thanh-Le Ha | Alexander Waibel
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

The correct translation of named entities (NEs) still poses a challenge for conventional neural machine translation (NMT) systems. This study explores methods incorporating named entity recognition (NER) into NMT with the aim to improve named entity translation. It proposes an annotation method that integrates named entities and inside–outside–beginning (IOB) tagging into the neural network input with the use of source factors. Our experiments on English→German and English→ Chinese show that just by including different NE classes and IOB tagging, we can increase the BLEU score by around 1 point using the standard test set from WMT2019 and achieve up to 12% increase in NE translation rates over a strong baseline.

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ELITR: European Live Translator
Ondřej Bojar | Dominik Macháček | Sangeet Sagar | Otakar Smrž | Jonáš Kratochvíl | Ebrahim Ansari | Dario Franceschini | Chiara Canton | Ivan Simonini | Thai-Son Nguyen | Felix Schneider | Sebastian Stücker | Alex Waibel | Barry Haddow | Rico Sennrich | Philip Williams
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

ELITR (European Live Translator) project aims to create a speech translation system for simultaneous subtitling of conferences and online meetings targetting up to 43 languages. The technology is tested by the Supreme Audit Office of the Czech Republic and by alfaview®, a German online conferencing system. Other project goals are to advance document-level and multilingual machine translation, automatic speech recognition, and automatic minuting.

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German-Arabic Speech-to-Speech Translation for Psychiatric Diagnosis
Juan Hussain | Mohammed Mediani | Moritz Behr | M. Amin Cheragui | Sebastian Stüker | Alexander Waibel
Proceedings of the Fifth Arabic Natural Language Processing Workshop

In this paper we present the natural language processing components of our German-Arabic speech-to-speech translation system which is being deployed in the context of interpretation during psychiatric, diagnostic interviews. For this purpose we have built a pipe-lined speech-to-speech translation system consisting of automatic speech recognition, text post-processing/segmentation, machine translation and speech synthesis systems. We have implemented two pipe-lines, from German to Arabic and Arabic to German, in order to be able to conduct interpreted two-way dialogues between psychiatrists and potential patients. All systems in our pipeline have been realized as all-neural end-to-end systems, using different architectures suitable for the different components. The speech recognition systems use an encoder/decoder + attention architecture, the text segmentation component and the machine translation system are based on the Transformer architecture, and for the speech synthesis systems we use Tacotron 2 for generating spectrograms and WaveGlow as vocoder. The speech translation is deployed in a server-based speech translation application that implements a turn based translation between a German speaking psychiatrist administrating the Mini-International Neuropsychiatric Interview (M.I.N.I.) and an Arabic speaking person answering the interview. As this is a very specific domain, in addition to the linguistic challenges posed by translating between Arabic and German, we also focus in this paper on the methods we implemented for adapting our speech translation system to the domain of this psychiatric interview.

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Removing European Language Barriers with Innovative Machine Translation Technology
Dario Franceschini | Chiara Canton | Ivan Simonini | Armin Schweinfurth | Adelheid Glott | Sebastian Stüker | Thai-Son Nguyen | Felix Schneider | Thanh-Le Ha | Alex Waibel | Barry Haddow | Philip Williams | Rico Sennrich | Ondřej Bojar | Sangeet Sagar | Dominik Macháček | Otakar Smrž
Proceedings of the 1st International Workshop on Language Technology Platforms

This paper presents our progress towards deploying a versatile communication platform in the task of highly multilingual live speech translation for conferences and remote meetings live subtitling. The platform has been designed with a focus on very low latency and high flexibility while allowing research prototypes of speech and text processing tools to be easily connected, regardless of where they physically run. We outline our architecture solution and also briefly compare it with the ELG platform. Technical details are provided on the most important components and we summarize the test deployment events we ran so far.

2019

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Incremental processing of noisy user utterances in the spoken language understanding task
Stefan Constantin | Jan Niehues | Alex Waibel
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

The state-of-the-art neural network architectures make it possible to create spoken language understanding systems with high quality and fast processing time. One major challenge for real-world applications is the high latency of these systems caused by triggered actions with high executions times. If an action can be separated into subactions, the reaction time of the systems can be improved through incremental processing of the user utterance and starting subactions while the utterance is still being uttered. In this work, we present a model-agnostic method to achieve high quality in processing incrementally produced partial utterances. Based on clean and noisy versions of the ATIS dataset, we show how to create datasets with our method to create low-latency natural language understanding components. We get improvements of up to 47.91 absolute percentage points in the metric F1-score.

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Fluent Translations from Disfluent Speech in End-to-End Speech Translation
Elizabeth Salesky | Matthias Sperber | Alexander Waibel
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Spoken language translation applications for speech suffer due to conversational speech phenomena, particularly the presence of disfluencies. With the rise of end-to-end speech translation models, processing steps such as disfluency removal that were previously an intermediate step between speech recognition and machine translation need to be incorporated into model architectures. We use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with disfluencies removed using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. We are able to directly generate fluent translations and introduce considerations about how to evaluate success on this task. This work provides a baseline for a new task, implicitly removing disfluencies in end-to-end translation of conversational speech.

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Improving Zero-shot Translation with Language-Independent Constraints
Ngoc-Quan Pham | Jan Niehues | Thanh-Le Ha | Alexander Waibel
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

An important concern in training multilingual neural machine translation (NMT) is to translate between language pairs unseen during training, i.e zero-shot translation. Improving this ability kills two birds with one stone by providing an alternative to pivot translation which also allows us to better understand how the model captures information between languages. In this work, we carried out an investigation on this capability of the multilingual NMT models. First, we intentionally create an encoder architecture which is independent with respect to the source language. Such experiments shed light on the ability of NMT encoders to learn multilingual representations, in general. Based on such proof of concept, we were able to design regularization methods into the standard Transformer model, so that the whole architecture becomes more robust in zero-shot conditions. We investigated the behaviour of such models on the standard IWSLT 2017 multilingual dataset. We achieved an average improvement of 2.23 BLEU points across 12 language pairs compared to the zero-shot performance of a state-of-the-art multilingual system. Additionally, we carry out further experiments in which the effect is confirmed even for language pairs with multiple intermediate pivots.

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Self-Attentional Models for Lattice Inputs
Matthias Sperber | Graham Neubig | Ngoc-Quan Pham | Alex Waibel
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Lattices are an efficient and effective method to encode ambiguity of upstream systems in natural language processing tasks, for example to compactly capture multiple speech recognition hypotheses, or to represent multiple linguistic analyses. Previous work has extended recurrent neural networks to model lattice inputs and achieved improvements in various tasks, but these models suffer from very slow computation speeds. This paper extends the recently proposed paradigm of self-attention to handle lattice inputs. Self-attention is a sequence modeling technique that relates inputs to one another by computing pairwise similarities and has gained popularity for both its strong results and its computational efficiency. To extend such models to handle lattices, we introduce probabilistic reachability masks that incorporate lattice structure into the model and support lattice scores if available. We also propose a method for adapting positional embeddings to lattice structures. We apply the proposed model to a speech translation task and find that it outperforms all examined baselines while being much faster to compute than previous neural lattice models during both training and inference.

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Paraphrases as Foreign Languages in Multilingual Neural Machine Translation
Zhong Zhou | Matthias Sperber | Alexander Waibel
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Paraphrases, rewordings of the same semantic meaning, are useful for improving generalization and translation. Unlike previous works that only explore paraphrases at the word or phrase level, we use different translations of the whole training data that are consistent in structure as paraphrases at the corpus level. We treat paraphrases as foreign languages, tag source sentences with paraphrase labels, and train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT). Our multi-paraphrase NMT that trains only on two languages outperforms the multilingual baselines. Adding paraphrases improves the rare word translation and increases entropy and diversity in lexical choice. Adding the source paraphrases boosts performance better than adding the target ones, while adding both lifts performance further. We achieve a BLEU score of 57.2 for French-to-English translation using 24 corpus-level paraphrases of the Bible, which outperforms the multilingual baselines and is +34.7 above the single-source single-target NMT baseline.

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Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
Matthias Sperber | Graham Neubig | Jan Niehues | Alex Waibel
Transactions of the Association for Computational Linguistics, Volume 7

Speech translation has traditionally been approached through cascaded models consisting of a speech recognizer trained on a corpus of transcribed speech, and a machine translation system trained on parallel texts. Several recent works have shown the feasibility of collapsing the cascade into a single, direct model that can be trained in an end-to-end fashion on a corpus of translated speech. However, experiments are inconclusive on whether the cascade or the direct model is stronger, and have only been conducted under the unrealistic assumption that both are trained on equal amounts of data, ignoring other available speech recognition and machine translation corpora. In this paper, we demonstrate that direct speech translation models require more data to perform well than cascaded models, and although they allow including auxiliary data through multi-task training, they are poor at exploiting such data, putting them at a severe disadvantage. As a remedy, we propose the use of end- to-end trainable models with two attention mechanisms, the first establishing source speech to source text alignments, the second modeling source to target text alignment. We show that such models naturally decompose into multi-task–trainable recognition and translation tasks and propose an attention-passing technique that alleviates error propagation issues in a previous formulation of a model with two attention stages. Our proposed model outperforms all examined baselines and is able to exploit auxiliary training data much more effectively than direct attentional models.

2018

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KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning
Florian Dessloch | Thanh-Le Ha | Markus Müller | Jan Niehues | Thai-Son Nguyen | Ngoc-Quan Pham | Elizabeth Salesky | Matthias Sperber | Sebastian Stüker | Thomas Zenkel | Alexander Waibel
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

In today’s globalized world we have the ability to communicate with people across the world. However, in many situations the language barrier still presents a major issue. For example, many foreign students coming to KIT to study are initially unable to follow a lecture in German. Therefore, we offer an automatic simultaneous interpretation service for students. To fulfill this task, we have developed a low-latency translation system that is adapted to lectures and covers several language pairs. While the switch from traditional Statistical Machine Translation to Neural Machine Translation (NMT) significantly improved performance, to integrate NMT into the speech translation framework required several adjustments. We have addressed the run-time constraints and different types of input. Furthermore, we utilized one-shot learning to easily add new topic-specific terms to the system. Besides better performance, NMT also enabled us increase our covered languages through multilingual NMT. % Combining these techniques, we are able to provide an adapted speech translation system for several European languages.

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Automated Evaluation of Out-of-Context Errors
Patrick Huber | Jan Niehues | Alex Waibel
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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BULBasaa: A Bilingual Basaa-French Speech Corpus for the Evaluation of Language Documentation Tools
Fatima Hamlaoui | Emmanuel-Moselly Makasso | Markus Müller | Jonas Engelmann | Gilles Adda | Alex Waibel | Sebastian Stüker
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus
Thanh-Le Ha | Jan Niehues | Matthias Sperber | Ngoc Quan Pham | Alexander Waibel
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Robust and Scalable Differentiable Neural Computer for Question Answering
Jörg Franke | Jan Niehues | Alex Waibel
Proceedings of the Workshop on Machine Reading for Question Answering

Deep learning models are often not easily adaptable to new tasks and require task-specific adjustments. The differentiable neural computer (DNC), a memory-augmented neural network, is designed as a general problem solver which can be used in a wide range of tasks. But in reality, it is hard to apply this model to new tasks. We analyze the DNC and identify possible improvements within the application of question answering. This motivates a more robust and scalable DNC (rsDNC). The objective precondition is to keep the general character of this model intact while making its application more reliable and speeding up its required training time. The rsDNC is distinguished by a more robust training, a slim memory unit and a bidirectional architecture. We not only achieve new state-of-the-art performance on the bAbI task, but also minimize the performance variance between different initializations. Furthermore, we demonstrate the simplified applicability of the rsDNC to new tasks with passable results on the CNN RC task without adaptions.

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Towards one-shot learning for rare-word translation with external experts
Ngoc-Quan Pham | Jan Niehues | Alexander Waibel
Proceedings of the 2nd Workshop on Neural Machine Translation and Generation

Neural machine translation (NMT) has significantly improved the quality of automatic translation models. One of the main challenges in current systems is the translation of rare words. We present a generic approach to address this weakness by having external models annotate the training data as Experts, and control the model-expert interaction with a pointer network and reinforcement learning. Our experiments using phrase-based models to simulate Experts to complement neural machine translation models show that the model can be trained to copy the annotations into the output consistently. We demonstrate the benefit of our proposed framework in outof domain translation scenarios with only lexical resources, improving more than 1.0 BLEU point in both translation directions English-Spanish and German-English.

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Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation
Zhong Zhou | Matthias Sperber | Alexander Waibel
Proceedings of the Third Conference on Machine Translation: Research Papers

We work on translation from rich-resource languages to low-resource languages. The main challenges we identify are the lack of low-resource language data, effective methods for cross-lingual transfer, and the variable-binding problem that is common in neural systems. We build a translation system that addresses these challenges using eight European language families as our test ground. Firstly, we add the source and the target family labels and study intra-family and inter-family influences for effective cross-lingual transfer. We achieve an improvement of +9.9 in BLEU score for English-Swedish translation using eight families compared to the single-family multi-source multi-target baseline. Moreover, we find that training on two neighboring families closest to the low-resource language is often enough. Secondly, we construct an ablation study and find that reasonably good results can be achieved even with considerably less target data. Thirdly, we address the variable-binding problem by building an order-preserving named entity translation model. We obtain 60.6% accuracy in qualitative evaluation where our translations are akin to human translations in a preliminary study.

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The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2018
Ngoc-Quan Pham | Jan Niehues | Alexander Waibel
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We present our experiments in the scope of the news translation task in WMT 2018, in directions: English→German. The core of our systems is the encoder-decoder based neural machine translation models using the transformer architecture. We enhanced the model with a deeper architecture. By using techniques to limit the memory consumption, we were able to train models that are 4 times larger on one GPU and improve the performance by 1.2 BLEU points. Furthermore, we performed sentence selection for the newly available ParaCrawl corpus. Thereby, we could improve the effectiveness of the corpus by 0.5 BLEU points.

2017

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Analyzing Neural MT Search and Model Performance
Jan Niehues | Eunah Cho | Thanh-Le Ha | Alex Waibel
Proceedings of the First Workshop on Neural Machine Translation

In this paper, we offer an in-depth analysis about the modeling and search performance. We address the question if a more complex search algorithm is necessary. Furthermore, we investigate the question if more complex models which might only be applicable during rescoring are promising. By separating the search space and the modeling using n-best list reranking, we analyze the influence of both parts of an NMT system independently. By comparing differently performing NMT systems, we show that the better translation is already in the search space of the translation systems with less performance. This results indicate that the current search algorithms are sufficient for the NMT systems. Furthermore, we could show that even a relatively small n-best list of 50 hypotheses already contain notably better translations.

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The QT21 Combined Machine Translation System for English to Latvian
Jan-Thorsten Peter | Hermann Ney | Ondřej Bojar | Ngoc-Quan Pham | Jan Niehues | Alex Waibel | Franck Burlot | François Yvon | Mārcis Pinnis | Valters Šics | Jasmijn Bastings | Miguel Rios | Wilker Aziz | Philip Williams | Frédéric Blain | Lucia Specia
Proceedings of the Second Conference on Machine Translation

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The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2017
Ngoc-Quan Pham | Jan Niehues | Thanh-Le Ha | Eunah Cho | Matthias Sperber | Alexander Waibel
Proceedings of the Second Conference on Machine Translation

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Neural Lattice-to-Sequence Models for Uncertain Inputs
Matthias Sperber | Graham Neubig | Jan Niehues | Alex Waibel
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The input to a neural sequence-to-sequence model is often determined by an up-stream system, e.g. a word segmenter, part of speech tagger, or speech recognizer. These up-stream models are potentially error-prone. Representing inputs through word lattices allows making this uncertainty explicit by capturing alternative sequences and their posterior probabilities in a compact form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a LatticeLSTM that is able to consume word lattices, and can be used as encoder in an attentional encoder-decoder model. We integrate lattice posterior scores into this architecture by extending the TreeLSTM’s child-sum and forget gates and introducing a bias term into the attention mechanism. We experiment with speech translation lattices and report consistent improvements over baselines that translate either the 1-best hypothesis or the lattice without posterior scores.

2016

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Evaluation of the KIT Lecture Translation System
Markus Müller | Sarah Fünfer | Sebastian Stüker | Alex Waibel
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

To attract foreign students is among the goals of the Karlsruhe Institute of Technology (KIT). One obstacle to achieving this goal is that lectures at KIT are usually held in German which many foreign students are not sufficiently proficient in, as, e.g., opposed to English. While the students from abroad are learning German during their stay at KIT, it is challenging to become proficient enough in it in order to follow a lecture. As a solution to this problem we offer our automatic simultaneous lecture translation. It translates German lectures into English in real time. While not as good as human interpreters, the system is available at a price that KIT can afford in order to offer it in potentially all lectures. In order to assess whether the quality of the system we have conducted a user study. In this paper we present this study, the way it was conducted and its results. The results indicate that the quality of the system has passed a threshold as to be able to support students in their studies. The study has helped to identify the most crucial weaknesses of the systems and has guided us which steps to take next.

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Optimizing Computer-Assisted Transcription Quality with Iterative User Interfaces
Matthias Sperber | Graham Neubig | Satoshi Nakamura | Alex Waibel
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Computer-assisted transcription promises high-quality speech transcription at reduced costs. This is achieved by limiting human effort to transcribing parts for which automatic transcription quality is insufficient. Our goal is to improve the human transcription quality via appropriate user interface design. We focus on iterative interfaces that allow humans to solve tasks based on an initially given suggestion, in this case an automatic transcription. We conduct a user study that reveals considerable quality gains for three variations of iterative interfaces over a non-iterative from-scratch transcription interface. Our iterative interfaces included post-editing, confidence-enhanced post-editing, and a novel retyping interface. All three yielded similar quality on average, but we found that the proposed retyping interface was less sensitive to the difficulty of the segment, and superior when the automatic transcription of the segment contained relatively many errors. An analysis using mixed-effects models allows us to quantify these and other factors and draw conclusions over which interface design should be chosen in which circumstance.

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Using Factored Word Representation in Neural Network Language Models
Jan Niehues | Thanh-Le Ha | Eunah Cho | Alex Waibel
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2016
Thanh-Le Ha | Eunah Cho | Jan Niehues | Mohammed Mediani | Matthias Sperber | Alexandre Allauzen | Alexander Waibel
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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The QT21/HimL Combined Machine Translation System
Jan-Thorsten Peter | Tamer Alkhouli | Hermann Ney | Matthias Huck | Fabienne Braune | Alexander Fraser | Aleš Tamchyna | Ondřej Bojar | Barry Haddow | Rico Sennrich | Frédéric Blain | Lucia Specia | Jan Niehues | Alex Waibel | Alexandre Allauzen | Lauriane Aufrant | Franck Burlot | Elena Knyazeva | Thomas Lavergne | François Yvon | Mārcis Pinnis | Stella Frank
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Lecture Translator - Speech translation framework for simultaneous lecture translation
Markus Müller | Thai Son Nguyen | Jan Niehues | Eunah Cho | Bastian Krüger | Thanh-Le Ha | Kevin Kilgour | Matthias Sperber | Mohammed Mediani | Sebastian Stüker | Alex Waibel
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

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Pre-Translation for Neural Machine Translation
Jan Niehues | Eunah Cho | Thanh-Le Ha | Alex Waibel
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Recently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT)-based systems, in some cases, the NMT system produces translations that have a completely different meaning. This is especially the case when rare words occur. When using statistical machine translation, it has already been shown that significant gains can be achieved by simplifying the input in a preprocessing step. A commonly used example is the pre-reordering approach. In this work, we used phrase-based machine translation to pre-translate the input into the target language. Then a neural machine translation system generates the final hypothesis using the pre-translation. Thereby, we use either only the output of the phrase-based machine translation (PBMT) system or a combination of the PBMT output and the source sentence. We evaluate the technique on the English to German translation task. Using this approach we are able to outperform the PBMT system as well as the baseline neural MT system by up to 2 BLEU points. We analyzed the influence of the quality of the initial system on the final result.

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Lightly Supervised Quality Estimation
Matthias Sperber | Graham Neubig | Jan Niehues | Sebastian Stüker | Alex Waibel
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Evaluating the quality of output from language processing systems such as machine translation or speech recognition is an essential step in ensuring that they are sufficient for practical use. However, depending on the practical requirements, evaluation approaches can differ strongly. Often, reference-based evaluation measures (such as BLEU or WER) are appealing because they are cheap and allow rapid quantitative comparison. On the other hand, practitioners often focus on manual evaluation because they must deal with frequently changing domains and quality standards requested by customers, for which reference-based evaluation is insufficient or not possible due to missing in-domain reference data (Harris et al., 2016). In this paper, we attempt to bridge this gap by proposing a framework for lightly supervised quality estimation. We collect manually annotated scores for a small number of segments in a test corpus or document, and combine them with automatically predicted quality scores for the remaining segments to predict an overall quality estimate. An evaluation shows that our framework estimates quality more reliably than using fully automatic quality estimation approaches, while keeping annotation effort low by not requiring full references to be available for the particular domain.

2015

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The Karlsruhe Institute of Technology Translation Systems for the WMT 2015
Eunah Cho | Thanh-Le Ha | Jan Niehues | Teresa Herrmann | Mohammed Mediani | Yuqi Zhang | Alex Waibel
Proceedings of the Tenth Workshop on Statistical Machine Translation

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The KIT-LIMSI Translation System for WMT 2015
Thanh-Le Ha | Quoc-Khanh Do | Eunah Cho | Jan Niehues | Alexandre Allauzen | François Yvon | Alex Waibel
Proceedings of the Tenth Workshop on Statistical Machine Translation

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ListNet-based MT Rescoring
Jan Niehues | Quoc Khanh Do | Alexandre Allauzen | Alex Waibel
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Evaluation of Crowdsourced User Input Data for Spoken Dialog Systems
Maria Schmidt | Markus Müller | Martin Wagner | Sebastian Stüker | Alex Waibel | Hansjörg Hofmann | Steffen Werner
Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Stripping Adjectives: Integration Techniques for Selective Stemming in SMT Systems
Isabel Slawik | Jan Niehues | Alex Waibel
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

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Stripping Adjectives: Integration Techniques for Selective Stemming in SMT Systems
Isabel Slawik | Jan Niehues | Alex Waibel
Proceedings of the 18th Annual Conference of the European Association for Machine Translation

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The KIT translation systems for IWSLT 2015
Thanh-Le Ha | Jan Niehues | Eunah Cho | Mohammed Mediani | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

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The 2015 KIT IWSLT speech-to-text systems for English and German
Markus Mueller | Tai Son Nguyen | Matthias Sperber | Kevin Kilgour | Sebastian Stuker | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

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Source discriminative word lexicon for translation disambiguation
Teresa Herrmann | Jan Niehues | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

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Multifeature modular deep neural network acoustic models
Kevin Kilgour | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

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Using language adaptive deep neural networks for improved multilingual speech recognition
Markus Mueller | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

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Punctuation insertion for real-time spoken language translation
Eunah Cho | Jan Niehues | Kevin Kilgour | Alex Waibel
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

2014

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Tight Integration of Speech Disfluency Removal into SMT
Eunah Cho | Jan Niehues | Alex Waibel
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Combining techniques from different NN-based language models for machine translation
Jan Niehues | Alexander Allauzen | François Yvon | Alex Waibel
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

This paper presents two improvements of language models based on Restricted Boltzmann Machine (RBM) for large machine translation tasks. In contrast to other continuous space approach, RBM based models can easily be integrated into the decoder and are able to directly learn a hidden representation of the n-gram. Previous work on RBM-based language models do not use a shared word representation and therefore, they might suffer of a lack of generalization for larger contexts. Moreover, since the training step is very time consuming, they are only used for quite small copora. In this work we add a shared word representation for the RBM-based language model by factorizing the weight matrix. In addition, we propose an efficient and tailored sampling algorithm that allows us to drastically speed up the training process. Experiments are carried out on two German to English translation tasks and the results show that the training time could be reduced by a factor of 10 without any drop in performance. Furthermore, the RBM-based model can also be trained on large size corpora.

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Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff
Matthias Sperber | Mirjam Simantzik | Graham Neubig | Satoshi Nakamura | Alex Waibel
Transactions of the Association for Computational Linguistics, Volume 2

In this paper, we study the problem of manually correcting automatic annotations of natural language in as efficient a manner as possible. We introduce a method for automatically segmenting a corpus into chunks such that many uncertain labels are grouped into the same chunk, while human supervision can be omitted altogether for other segments. A tradeoff must be found for segment sizes. Choosing short segments allows us to reduce the number of highly confident labels that are supervised by the annotator, which is useful because these labels are often already correct and supervising correct labels is a waste of effort. In contrast, long segments reduce the cognitive effort due to context switches. Our method helps find the segmentation that optimizes supervision efficiency by defining user models to predict the cost and utility of supervising each segment and solving a constrained optimization problem balancing these contradictory objectives. A user study demonstrates noticeable gains over pre-segmented, confidence-ordered baselines on two natural language processing tasks: speech transcription and word segmentation.

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The KIT-LIMSI Translation System for WMT 2014
Quoc Khanh Do | Teresa Herrmann | Jan Niehues | Alexander Allauzen | François Yvon | Alex Waibel
Proceedings of the Ninth Workshop on Statistical Machine Translation

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EU-BRIDGE MT: Combined Machine Translation
Markus Freitag | Stephan Peitz | Joern Wuebker | Hermann Ney | Matthias Huck | Rico Sennrich | Nadir Durrani | Maria Nadejde | Philip Williams | Philipp Koehn | Teresa Herrmann | Eunah Cho | Alex Waibel
Proceedings of the Ninth Workshop on Statistical Machine Translation

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The Karlsruhe Institute of Technology Translation Systems for the WMT 2014
Teresa Herrmann | Mohammed Mediani | Eunah Cho | Thanh-Le Ha | Jan Niehues | Isabel Slawik | Yuqi Zhang | Alex Waibel
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Combined spoken language translation
Markus Freitag | Joern Wuebker | Stephan Peitz | Hermann Ney | Matthias Huck | Alexandra Birch | Nadir Durrani | Philipp Koehn | Mohammed Mediani | Isabel Slawik | Jan Niehues | Eunach Cho | Alex Waibel | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

EU-BRIDGE is a European research project which is aimed at developing innovative speech translation technology. One of the collaborative efforts within EU-BRIDGE is to produce joint submissions of up to four different partners to the evaluation campaign at the 2014 International Workshop on Spoken Language Translation (IWSLT). We submitted combined translations to the German→English spoken language translation (SLT) track as well as to the German→English, English→German and English→French machine translation (MT) tracks. In this paper, we present the techniques which were applied by the different individual translation systems of RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show the combination approach developed at RWTH Aachen University which combined the individual systems. The consensus translations yield empirical gains of up to 2.3 points in BLEU and 1.2 points in TER compared to the best individual system.

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The 2014 KIT IWSLT speech-to-text systems for English, German and Italian
Kevin Kilgour | Michael Heck | Markus Müller | Matthias Sperber | Sebastian Stüker | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our German, Italian and English Speech-to-Text (STT) systems for the 2014 IWSLT TED ASR track. Our setup uses ROVER and confusion network combination from various subsystems to achieve a good overall performance. The individual subsystems are built by using different front-ends, (e.g., MVDR-MFCC or lMel), acoustic models (GMM or modular DNN) and phone sets and by training on various subsets of the training data. Decoding is performed in two stages, where the GMM systems are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cMLLR. The combination setup produces a final hypothesis that has a significantly lower WER than any of the individual subsystems.

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The KIT translation systems for IWSLT 2014
Isabel Slawik | Mohammed Mediani | Jan Niehues | Yuqi Zhang | Eunah Cho | Teresa Herrmann | Thanh-Le Ha | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we present the KIT systems participating in the TED translation tasks of the IWSLT 2014 machine translation evaluation. We submitted phrase-based translation systems for all three official directions, namely English→German, German→English, and English→French, as well as for the optional directions English→Chinese and English→Arabic. For the official directions we built systems both for the machine translation as well as the spoken language translation track. This year we improved our systems’ performance over last year through n-best list rescoring using neural network-based translation and language models and novel preordering rules based on tree information of multiple syntactic levels. Furthermore, we could successfully apply a novel phrase extraction algorithm and transliteration of unknown words for Arabic. We also submitted a contrastive system for German→English built with stemmed German adjectives. For the SLT tracks, we used a monolingual translation system to translate the lowercased ASR hypotheses with all punctuation stripped to truecased, punctuated output as a preprocessing step to our usual translation system.

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Machine translation of multi-party meetings: segmentation and disfluency removal strategies
Eunah Cho | Jan Niehues | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Translating meetings presents a challenge since multi-speaker speech shows a variety of disfluencies. In this paper we investigate the importance of transforming speech into well-written input prior to translating multi-party meetings. We first analyze the characteristics of this data and establish oracle scores. Sentence segmentation and punctuation are performed using a language model, turn information, or a monolingual translation system. Disfluencies are removed by a CRF model trained on in-domain and out-of-domain data. For comparison, we build a combined CRF model for punctuation insertion and disfluency removal. By applying these models, multi-party meetings are transformed into fluent input for machine translation. We evaluate the models with regard to translation performance and are able to achieve an improvement of 2.1 to 4.9 BLEU points depending on the availability of turn information.

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Extracting translation pairs from social network content
Matthias Eck | Yuri Zemlyanskiy | Joy Zhang | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

We introduce two methods to collect additional training data for statistical machine translation systems from public social network content. The first method identifies multilingual content where the author self-translated their own post to reach additional friends, fans or customers. Once identified, we can split the post in the language segments and extract translation pairs from this content. The second methods considers web links (URLs) that users add as part of their post to point the reader to a video, article or website. If the same URL is shared from different language users, there is a chance they might give the same comment in their respective language. We use a support vector machine (SVM) as a classifier to identify true translations from all candidate pairs. We collected additional translation pairs using both methods for the language pairs Spanish-English and Portuguese-English. Testing the collected data as additional training data for statistical machine translations on in-domain test sets resulted in very significant improvements of up to 5 BLEU.

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Lexical translation model using a deep neural network architecture
Thanh-Le Ha | Jan Niehues | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

In this paper we combine the advantages of a model using global source sentence contexts, the Discriminative Word Lexicon, and neural networks. By using deep neural networks instead of the linear maximum entropy model in the Discriminative Word Lexicon models, we are able to leverage dependencies between different source words due to the non-linearity. Furthermore, the models for different target words can share parameters and therefore data sparsity problems are effectively reduced. By using this approach in a state-of-the-art translation system, we can improve the performance by up to 0.5 BLEU points for three different language pairs on the TED translation task.

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Improving in-domain data selection for small in-domain sets
Mohammed Mediani | Joshua Winebarger | Alexander Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Finding sufficient in-domain text data for language modeling is a recurrent challenge. Some methods have already been proposed for selecting parts of out-of-domain text data most closely resembling the in-domain data using a small amount of the latter. Including this new “near-domain” data in training can potentially lead to better language model performance, while reducing training resources relative to incorporating all data. One popular, state-of-the-art selection process based on cross-entropy scores makes use of in-domain and out-ofdomain language models. In order to compensate for the limited availability of the in-domain data required for this method, we introduce enhancements to two of its steps. Firstly, we improve the procedure for drawing the outof-domain sample data used for selection. Secondly, we use word-associations in order to extend the underlying vocabulary of the sample language models used for scoring. These enhancements are applied to selecting text for language modeling of talks given in a technical subject area. Besides comparing perplexity, we judge the resulting language models by their performance in automatic speech recognition and machine translation tasks. We evaluate our method in different contexts. We show that it yields consistent improvements, up to 2% absolute reduction in word error rate and 0.3 Bleu points. We achieve these improvements even given a much smaller in-domain set.

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Multilingual deep bottle neck features: a study on language selection and training techniques
Markus Müller | Sebastian Stüker | Zaid Sheikh | Florian Metze | Alex Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

Previous work has shown that training the neural networks for bottle neck feature extraction in a multilingual way can lead to improvements in word error rate and average term weighted value in a telephone key word search task. In this work we conduct a systematic study on a) which multilingual training strategy to employ, b) the effect of language selection and amount of multilingual training data used and c) how to find a suitable combination for languages. We conducted our experiment on the key word search task and the languages of the IARPA BABEL program. In a first step, we assessed the performance of a single language out of all available languages in combination with the target language. Based on these results, we then combined a multitude of languages. We also examined the influence of the amount of training data per language, as well as different techniques for combining the languages during network training. Our experiments show that data from arbitrary additional languages does not necessarily increase the performance of a system. But when combining a suitable set of languages, a significant gain in performance can be achieved.

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Rule-based preordering on multiple syntactic levels in statistical machine translation
Ge Wu | Yuqi Zhang | Alexander Waibel
Proceedings of the 11th International Workshop on Spoken Language Translation: Papers

We propose a novel data-driven rule-based preordering approach, which uses the tree information of multiple syntactic levels. This approach extend the tree-based reordering from one level into multiple levels, which has the capability to process more complicated reordering cases. We have conducted experiments in English-to-Chinese and Chinese-to-English translation directions. Our results show that the approach has led to improved translation quality both when it was applied separately or when it was combined with some other reordering approaches. As our reordering approach was used alone, it showed an improvement of 1.61 in BLEU score in the English-to-Chinese translation direction and an improvement of 2.16 in BLEU score in the Chinese-to-English translation direction, in comparison with the baseline, which used no word reordering. As our preordering approach were combined with the short rule [1], long rule [2] and tree rule [3] based preordering approaches, it showed further improvements of up to 0.43 in BLEU score in the English-to-Chinese translation direction and further improvements of up to 0.3 in BLEU score in the Chinese-to-English translation direction. Through the translations that used our preordering approach, we have also found many translation examples with improved syntactic structures.

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A Corpus of Spontaneous Speech in Lectures: The KIT Lecture Corpus for Spoken Language Processing and Translation
Eunah Cho | Sarah Fünfer | Sebastian Stüker | Alex Waibel
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

With the increasing number of applications handling spontaneous speech, the needs to process spoken languages become stronger. Speech disfluency is one of the most challenging tasks to deal with in automatic speech processing. As most applications are trained with well-formed, written texts, many issues arise when processing spontaneous speech due to its distinctive characteristics. Therefore, more data with annotated speech disfluencies will help the adaptation of natural language processing applications, such as machine translation systems. In order to support this, we have annotated speech disfluencies in German lectures at KIT. In this paper we describe how we annotated the disfluencies in the data and provide detailed statistics on the size of the corpus and the speakers. Moreover, machine translation performance on a source text including disfluencies is compared to the results of the translation of a source text without different sorts of disfluencies or no disfluencies at all.

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Manual Analysis of Structurally Informed Reordering in German-English Machine Translation
Teresa Herrmann | Jan Niehues | Alex Waibel
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Word reordering is a difficult task for translation. Common automatic metrics such as BLEU have problems reflecting improvements in target language word order. However, it is a crucial aspect for humans when deciding on translation quality. This paper presents a detailed analysis of a structure-aware reordering approach applied in a German-to-English phrase-based machine translation system. We compare the translation outputs of two translation systems applying reordering rules based on parts-of-speech and syntax trees on a sentence-by-sentence basis. For each sentence-pair we examine the global translation performance and classify local changes in the translated sentences. This analysis is applied to three data sets representing different genres. While the improvement in BLEU differed substantially between the data sets, the manual evaluation showed that both global translation performance as well as individual types of improvements and degradations exhibit a similar behavior throughout the three data sets. We have observed that for 55-64% of the sentences with different translations, the translation produced using the tree-based reordering was considered to be the better translation. As intended by the investigated reordering model, most improvements are achieved by improving the position of the verb or being able to translate a verb that could not be translated before.

2013

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Measuring the Structural Importance through Rhetorical Structure Index
Narine Kokhlikyan | Alex Waibel | Yuqi Zhang | Joy Ying Zhang
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Combining Word Reordering Methods on different Linguistic Abstraction Levels for Statistical Machine Translation
Teresa Herrmann | Jan Niehues | Alex Waibel
Proceedings of the Seventh Workshop on Syntax, Semantics and Structure in Statistical Translation

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The Karlsruhe Institute of Technology Translation Systems for the WMT 2013
Eunah Cho | Thanh-Le Ha | Mohammed Mediani | Jan Niehues | Teresa Herrmann | Isabel Slawik | Alex Waibel
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Joint WMT 2013 Submission of the QUAERO Project
Stephan Peitz | Saab Mansour | Matthias Huck | Markus Freitag | Hermann Ney | Eunah Cho | Teresa Herrmann | Mohammed Mediani | Jan Niehues | Alex Waibel | Alexander Allauzen | Quoc Khanh Do | Bianka Buschbeck | Tonio Wandmacher
Proceedings of the Eighth Workshop on Statistical Machine Translation

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An MT Error-Driven Discriminative Word Lexicon using Sentence Structure Features
Jan Niehues | Alex Waibel
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Letter N-Gram-based Input Encoding for Continuous Space Language Models
Henning Sperr | Jan Niehues | Alex Waibel
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality

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The 2013 KIT IWSLT speech-to-text systems for German and English
Kevin Kilgour | Christian Mohr | Michael Heck | Quoc Bao Nguyen | Van Huy Nguyen | Evgeniy Shin | Igor Tseyzer | Jonas Gehring | Markus Müller | Matthias Sperber | Sebastian Stüker | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our English Speech-to-Text (STT) systems for the 2013 IWSLT TED ASR track. The systems consist of multiple subsystems that are combinations of different front-ends, e.g. MVDR-MFCC based and lMel based ones, GMM and NN acoustic models and different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cMLLR.

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EU-BRIDGE MT: text translation of talks in the EU-BRIDGE project
Markus Freitag | Stephan Peitz | Joern Wuebker | Hermann Ney | Nadir Durrani | Matthias Huck | Philipp Koehn | Thanh-Le Ha | Jan Niehues | Mohammed Mediani | Teresa Herrmann | Alex Waibel | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

EU-BRIDGE1 is a European research project which is aimed at developing innovative speech translation technology. This paper describes one of the collaborative efforts within EUBRIDGE to further advance the state of the art in machine translation between two European language pairs, English→French and German→English. Four research institutions involved in the EU-BRIDGE project combined their individual machine translation systems and participated with a joint setup in the machine translation track of the evaluation campaign at the 2013 International Workshop on Spoken Language Translation (IWSLT). We present the methods and techniques to achieve high translation quality for text translation of talks which are applied at RWTH Aachen University, the University of Edinburgh, Karlsruhe Institute of Technology, and Fondazione Bruno Kessler. We then show how we have been able to considerably boost translation performance (as measured in terms of the metrics BLEU and TER) by means of system combination. The joint setups yield empirical gains of up to 1.4 points in BLEU and 2.8 points in TER on the IWSLT test sets compared to the best single systems.

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The KIT translation systems for IWSLT 2013
Than-Le Ha | Teresa Herrmann | Jan Niehues | Mohammed Mediani | Eunah Cho | Yuqi Zhang | Isabel Slawik | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we present the KIT systems participating in all three official directions, namely English→German, German→English, and English→French, in translation tasks of the IWSLT 2013 machine translation evaluation. Additionally, we present the results for our submissions to the optional directions English→Chinese and English→Arabic. We used phrase-based translation systems to generate the translations. This year, we focused on adapting the systems towards ASR input. Furthermore, we investigated different reordering models as well as an extended discriminative word lexicon. Finally, we added a data selection approach for domain adaptation.

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The 2013 KIT Quaero speech-to-text system for French
Joshua Winebarger | Bao Nguyen | Jonas Gehring | Sebastian Stüker | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

This paper describes our Speech-to-Text (STT) system for French, which was developed as part of our efforts in the Quaero program for the 2013 evaluation. Our STT system consists of six subsystems which were created by combining multiple complementary sources of pronunciation modeling including graphemes with various feature front-ends based on deep neural networks and tonal features. Both speaker-independent and speaker adaptively trained versions of the systems were built. The resulting systems were then combined via confusion network combination and crossadaptation. Through progressive advances and system combination we reach a word error rate (WER) of 16.5% on the 2012 Quaero evaluation data.

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Incremental unsupervised training for university lecture recognition
Michael Heck | Sebastian Stüker | Sakriani Sakti | Alex Waibel | Satoshi Nakamura
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

In this paper we describe our work on unsupervised adaptation of the acoustic model of our simultaneous lecture translation system. We trained a speaker independent acoustic model, with which we produce automatic transcriptions of new lectures in order to improve the system for a specific lecturer. We compare our results against a model that was trained in a supervised way on an exact manual transcription. We examine four different ways of processing the decoder outputs of the automatic transcription with respect to the treatment of pronunciation variants and noise words. We will show that, instead of fixating the latter informations in the transcriptions, it is of advantage to let the Viterbi algorithm during training decide which pronunciations to use and where to insert which noise words. Further, we utilize word level posterior probabilities obtained during decoding by weighting and thresholding the words of a transcription.

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Analyzing the potential of source sentence reordering in statistical machine translation
Teresa Herrmann | Jochen Weiner | Jan Niehues | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

We analyze the performance of source sentence reordering, a common reordering approach, using oracle experiments on German-English and English-German translation. First, we show that the potential of this approach is very promising. Compared to a monotone translation, the optimally reordered source sentence leads to improvements of up to 4.6 and 6.2 BLEU points, depending on the language. Furthermore, we perform a detailed evaluation of the different aspects of the approach. We analyze the impact of the restriction of the search space by reordering lattices and we can show that using more complex rule types for reordering results in better approximation of the optimally reordered source. However, a gap of about 3 to 3.8 BLEU points remains, presenting a promising perspective for research on extending the search space through better reordering rules. When evaluating the ranking of different reordering variants, the results reveal that the search for the best path in the lattice performs very well for German-English translation. For English-German translation there is potential for an improvement of up to 1.4 BLEU points through a better ranking of the different reordering possibilities in the reordering lattice.

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CRF-based disfluency detection using semantic features for German to English spoken language translation
Eunah Cho | Than-Le Ha | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Disfluencies in speech pose severe difficulties in machine translation of spontaneous speech. This paper presents our conditional random field (CRF)-based speech disfluency detection system developed on German to improve spoken language translation performance. In order to detect speech disfluencies considering syntactics and semantics of speech utterances, we carried out a CRF-based approach using information learned from the word representation and the phrase table used for machine translation. The word representation is gained using recurrent neural networks and projected words are clustered using the k-means algorithm. Using the output from the model trained with the word representations and phrase table information, we achieve an improvement of 1.96 BLEU points on the lecture test set. By keeping or removing humanannotated disfluencies, we show an upper bound and lower bound of translation quality. In an oracle experiment we gain 3.16 BLEU points of improvement on the lecture test set, compared to the same set with all disfluencies.

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Maximum entropy language modeling for Russian ASR
Evgeniy Shin | Sebastian Stüker | Kevin Kilgour | Christian Fügen | Alex Waibel
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

Russian is a challenging language for automatic speech recognition systems due to its rich morphology. This rich morphology stems from Russian’s highly inflectional nature and the frequent use of preand suffixes. Also, Russian has a very free word order, changes in which are used to reflect connotations of the sentences. Dealing with these phenomena is rather difficult for traditional n-gram models. We therefore investigate in this paper the use of a maximum entropy language model for Russian whose features are specifically designed to deal with the inflections in Russian, as well as the loose word order. We combine this with a subword based language model in order to alleviate the problem of large vocabulary sizes necessary for dealing with highly inflecting languages. Applying the maximum entropy language model during re-scoring improves the word error rate of our recognition system by 1.2% absolute, while the use of the sub-word based language model reduces the vocabulary size from 120k to 40k and the OOV rate from 4.8% to 2.1%.

2012

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The KIT translation systems for IWSLT 2012
Mohammed Mediani | Yuqi Zhang | Thanh-Le Ha | Jan Niehues | Eunach Cho | Teresa Herrmann | Rainer Kärgel | Alexander Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

In this paper, we present the KIT systems participating in the English-French TED Translation tasks in the framework of the IWSLT 2012 machine translation evaluation. We also present several additional experiments on the English-German, English-Chinese and English-Arabic translation pairs. Our system is a phrase-based statistical machine translation system, extended with many additional models which were proven to enhance the translation quality. For instance, it uses the part-of-speech (POS)-based reordering, translation and language model adaptation, bilingual language model, word-cluster language model, discriminative word lexica (DWL), and continuous space language model. In addition to this, the system incorporates special steps in the preprocessing and in the post-processing step. In the preprocessing the noisy corpora are filtered by removing the noisy sentence pairs, whereas in the postprocessing the agreement between a noun and its surrounding words in the French translation is corrected based on POS tags with morphological information. Our system deals with speech transcription input by removing case information and punctuation except periods from the text translation model.

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The 2012 KIT and KIT-NAIST English ASR systems for the IWSLT evaluation
Christian Saam | Christian Mohr | Kevin Kilgour | Michael Heck | Matthias Sperber | Keigo Kubo | Sebatian Stüker | Sakriani Sakri | Graham Neubig | Tomoki Toda | Satoshi Nakamura | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our English Speech-to-Text (STT) systems for the 2012 IWSLT TED ASR track evaluation. The systems consist of 10 subsystems that are combinations of different front-ends, e.g. MVDR based and MFCC based ones, and two different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two stages, where the systems of the second stage are adapted in an unsupervised manner on the combination of the first stage outputs using VTLN, MLLR, and cM-LLR.

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The KIT-NAIST (contrastive) English ASR system for IWSLT 2012
Michael Heck | Keigo Kubo | Matthias Sperber | Sakriani Sakti | Sebastian Stüker | Christian Saam | Kevin Kilgour | Christian Mohr | Graham Neubig | Tomoki Toda | Satoshi Nakamura | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the KIT-NAIST (Contrastive) English speech recognition system for the IWSLT 2012 Evaluation Campaign. In particular, we participated in the ASR track of the IWSLT TED task. The system was developed by Karlsruhe Institute of Technology (KIT) and Nara Institute of Science and Technology (NAIST) teams in collaboration within the interACT project. We employ single system decoding with fully continuous and semi-continuous models, as well as a three-stage, multipass system combination framework built with the Janus Recognition Toolkit. On the IWSLT 2010 test set our single system introduced in this work achieves a WER of 17.6%, and our final combination achieves a WER of 14.4%.

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Continuous space language models using restricted Boltzmann machines
Jan Niehues | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

We present a novel approach for continuous space language models in statistical machine translation by using Restricted Boltzmann Machines (RBMs). The probability of an n-gram is calculated by the free energy of the RBM instead of a feedforward neural net. Therefore, the calculation is much faster and can be integrated into the translation process instead of using the language model only in a re-ranking step. Furthermore, it is straightforward to introduce additional word factors into the language model. We observed a faster convergence in training if we include automatically generated word classes as an additional word factor. We evaluated the RBM-based language model on the German to English and English to French translation task of TED lectures. Instead of replacing the conventional n-gram-based language model, we trained the RBM-based language model on the more important but smaller in-domain data and combined them in a log-linear way. With this approach we could show improvements of about half a BLEU point on the translation task.

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Evaluation of interactive user corrections for lecture transcription
Heinrich Kolkhorst | Kevin Kilgour | Sebastian Stüker | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

In this work, we present and evaluate the usage of an interactive web interface for browsing and correcting lecture transcripts. An experiment performed with potential users without transcription experience provides us with a set of example corrections. On German lecture data, user corrections greatly improve the comprehensibility of the transcripts, yet only reduce the WER to 22%. The precision of user edits is relatively low at 77% and errors in inflection, case and compounds were rarely corrected. Nevertheless, characteristic lecture data errors, such as highly specific terms, were typically corrected, providing valuable additional information.

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Segmentation and punctuation prediction in speech language translation using a monolingual translation system
Eunah Cho | Jan Niehues | Alex Waibel
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

In spoken language translation (SLT), finding proper segmentation and reconstructing punctuation marks are not only significant but also challenging tasks. In this paper we present our recent work on speech translation quality analysis for German-English by improving sentence segmentation and punctuation. From oracle experiments, we show an upper bound of translation quality if we had human-generated segmentation and punctuation on the output stream of speech recognition systems. In our oracle experiments we gain 1.78 BLEU points of improvements on the lecture test set. We build a monolingual translation system from German to German implementing segmentation and punctuation prediction as a machine translation task. Using the monolingual translation system we get an improvement of 1.53 BLEU points on the lecture test set, which is a comparable performance against the upper bound drawn by the oracle experiments.

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Joint WMT 2012 Submission of the QUAERO Project
Markus Freitag | Stephan Peitz | Matthias Huck | Hermann Ney | Jan Niehues | Teresa Herrmann | Alex Waibel | Hai-son Le | Thomas Lavergne | Alexandre Allauzen | Bianka Buschbeck | Josep Maria Crego | Jean Senellart
Proceedings of the Seventh Workshop on Statistical Machine Translation

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The Karlsruhe Institute of Technology Translation Systems for the WMT 2012
Jan Niehues | Yuqi Zhang | Mohammed Mediani | Teresa Herrmann | Eunah Cho | Alex Waibel
Proceedings of the Seventh Workshop on Statistical Machine Translation

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The KIT Lecture Corpus for Speech Translation
Sebastian Stüker | Florian Kraft | Christian Mohr | Teresa Herrmann | Eunah Cho | Alex Waibel
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Academic lectures offer valuable content, but often do not reach their full potential audience due to the language barrier. Human translations of lectures are too expensive to be widely used. Speech translation technology can be an affordable alternative in this case. State-of-the-art speech translation systems utilize statistical models that need to be trained on large amounts of in-domain data. In order to support the KIT lecture translation project in its effort to introduce speech translation technology in KIT's lecture halls, we have collected a corpus of German lectures at KIT. In this paper we describe how we recorded the lectures and how we annotated them. We further give detailed statistics on the types of lectures in the corpus and its size. We collected the corpus with the purpose in mind that it should not just be suited for training a spoken language translation system the traditional way, but should also enable us to research techniques that enable the translation system to automatically and autonomously adapt itself to the varying topics and speakers of lectures

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Detailed Analysis of Different Strategies for Phrase Table Adaptation in SMT
Jan Niehues | Alex Waibel
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

This paper gives a detailed analysis of different approaches to adapt a statistical machine translation system towards a target domain using small amounts of parallel in-domain data. Therefore, we investigate the differences between the approaches addressing adaptation on the two main steps of building a translation model: The candidate selection and the phrase scoring. For the latter step we characterized the differences by four key aspects. We performed experiments on two different tasks of speech translation and analyzed the influence of the different aspects on the overall translation quality. On both tasks we could show significant improvements by using the presented adaptation techniques.

2011

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Wider Context by Using Bilingual Language Models in Machine Translation
Jan Niehues | Teresa Herrmann | Stephan Vogel | Alex Waibel
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Joint WMT Submission of the QUAERO Project
Markus Freitag | Gregor Leusch | Joern Wuebker | Stephan Peitz | Hermann Ney | Teresa Herrmann | Jan Niehues | Alex Waibel | Alexandre Allauzen | Gilles Adda | Josep Maria Crego | Bianka Buschbeck | Tonio Wandmacher | Jean Senellart
Proceedings of the Sixth Workshop on Statistical Machine Translation

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The Karlsruhe Institute of Technology Translation Systems for the WMT 2011
Teresa Herrmann | Mohammed Mediani | Jan Niehues | Alex Waibel
Proceedings of the Sixth Workshop on Statistical Machine Translation

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TriS: A Statistical Sentence Simplifier with Log-linear Models and Margin-based Discriminative Training
Nguyen Bach | Qin Gao | Stephan Vogel | Alex Waibel
Proceedings of 5th International Joint Conference on Natural Language Processing

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Unsupervised Vocabulary Selection for Domain-Independent Simultaneous Lecture Translation
Paul Maergner | Ian Lane | Alex Waibel
Proceedings of Machine Translation Summit XIII: Papers

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The KIT English-French translation systems for IWSLT 2011
Mohammed Mediani | Eunach Cho | Jan Niehues | Teresa Herrmann | Alex Waibel
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper presents the KIT system participating in the English→French TALK Translation tasks in the framework of the IWSLT 2011 machine translation evaluation. Our system is a phrase-based translation system using POS-based reordering extended with many additional features. First of all, a special preprocessing is devoted to the Giga corpus in order to minimize the effect of the great amount of noise it contains. In addition, the system gives more importance to the in-domain data by adapting the translation and the language models as well as by using a wordcluster language model. Furthermore, the system is extended by a bilingual language model and a discriminative word lexicon. The automatic speech transcription input usually has no or wrong punctuation marks, therefore these marks were especially removed from the source training data for the SLT system training.

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The 2011 KIT English ASR system for the IWSLT evaluation
Sebastian Stüker | Kevin Kilgour | Christian Saam | Alex Waibel
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes our English Speech-to-Text (STT) system for the 2011 IWSLT ASR track. The system consists of 2 subsystems with different front-ends—one MVDR based, one MFCC based—which are combined using confusion network combination to provide a base for a second pass speaker adapted MVDR system. We demonstrate that this set-up produces competitive results on the IWSLT 2010 dev and test sets.

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Advances on spoken language translation in the Quaero program
Karim Boudahmane | Bianka Buschbeck | Eunah Cho | Josep Maria Crego | Markus Freitag | Thomas Lavergne | Hermann Ney | Jan Niehues | Stephan Peitz | Jean Senellart | Artem Sokolov | Alex Waibel | Tonio Wandmacher | Joern Wuebker | François Yvon
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

The Quaero program is an international project promoting research and industrial innovation on technologies for automatic analysis and classification of multimedia and multilingual documents. Within the program framework, research organizations and industrial partners collaborate to develop prototypes of innovating applications and services for access and usage of multimedia data. One of the topics addressed is the translation of spoken language. Each year, a project-internal evaluation is conducted by DGA to monitor the technological advances. This work describes the design and results of the 2011 evaluation campaign. The participating partners were RWTH, KIT, LIMSI and SYSTRAN. Their approaches are compared on both ASR output and reference transcripts of speech data for the translation between French and German. The results show that the developed techniques further the state of the art and improve translation quality.

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Speech recognition for machine translation in Quaero
Lori Lamel | Sandrine Courcinous | Julien Despres | Jean-Luc Gauvain | Yvan Josse | Kevin Kilgour | Florian Kraft | Viet-Bac Le | Hermann Ney | Markus Nußbaum-Thom | Ilya Oparin | Tim Schlippe | Ralf Schlüter | Tanja Schultz | Thiago Fraga da Silva | Sebastian Stüker | Martin Sundermeyer | Bianca Vieru | Ngoc Thang Vu | Alexander Waibel | Cécile Woehrling
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the speech-to-text systems used to provide automatic transcriptions used in the Quaero 2010 evaluation of Machine Translation from speech. Quaero (www.quaero.org) is a large research and industrial innovation program focusing on technologies for automatic analysis and classification of multimedia and multilingual documents. The ASR transcript is the result of a Rover combination of systems from three teams ( KIT, RWTH, LIMSI+VR) for the French and German languages. The casesensitive word error rates (WER) of the combined systems were respectively 20.8% and 18.1% on the 2010 evaluation data, relative WER reductions of 14.6% and 17.4% respectively over the best component system.

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The 2011 KIT QUAERO speech-to-text system for Spanish
Kevin Kilgour | Christian Saam | Christian Mohr | Sebastian Stüker | Alex Waibel
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

This paper describes our current Spanish speech-to-text (STT) system with which we participated in the 2011 Quaero STT evaluation that is being developed within the Quaero program. The system consists of 4 separate subsystems, as well as the standard MFCC and MVDR phoneme based subsystems we included a both a phoneme and grapheme based bottleneck subsystem. We carefully evaluate the performance of each subsystem. After including several new techniques we were able to reduce the WER by over 30% from 20.79% to 14.53%.

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Unsupervised vocabulary selection for simultaneous lecture translation
Paul Maergner | Kevin Kilgour | Ian Lane | Alex Waibel
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

In this work, we propose a novel method for vocabulary selection which enables simultaneous speech recognition systems for lectures to automatically adapt to the diverse topics that occur in educational and scientific lectures. Utilizing materials that are available before the lecture begins, such as lecture slides, our proposed framework iteratively searches for related documents on the World Wide Web and generates a lecture-specific vocabulary and language model based on the resulting documents. In this paper, we introduce a novel method for vocabulary selection where we rank vocabulary that occurs in the collected documents based on a relevance score which is calculated using a combination of word features. Vocabulary selection is a critical component for topic adaptation that has typically been overlooked in prior works. On the interACT German-English simultaneous lecture translation system our proposed approach significantly improved vocabulary coverage, reducing the out-of-vocabulary rate on average by 57.0% and up to 84.9%, compared to a lecture-independent baseline. Furthermore, our approach reduced the word error rate by up to 25.3% (on average 13.2% across all lectures), compared to a lectureindependent baseline.

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Using Wikipedia to translate domain-specific terms in SMT
Jan Niehues | Alex Waibel
Proceedings of the 8th International Workshop on Spoken Language Translation: Papers

When building a university lecture translation system, one important step is to adapt it to the target domain. One problem in this adaptation task is to acquire translations for domain specific terms. In this approach we tried to get these translations from Wikipedia, which provides articles on very specific topics in many different languages. To extract translations for the domain specific terms, we used the interlanguage links of Wikipedia . We analyzed different methods to integrate this corpus into our system and explored methods to disambiguate between different translations by using the text of the articles. In addition, we developed methods to handle different morphological forms of the specific terms in morphologically rich input languages like German. The results show that the number of out-of-vocabulary (OOV) words could be reduced by 50% on computer science lectures and the translation quality could be improved by more than 1 BLEU point.

2010

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Domain Adaptation in Statistical Machine Translation using Factored Translation Models
Jan Niehues | Alex Waibel
Proceedings of the 14th Annual conference of the European Association for Machine Translation

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Tools for Collecting Speech Corpora via Mechanical-Turk
Ian Lane | Matthias Eck | Kay Rottmann | Alex Waibel
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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The Karlsruhe Institute for Technology Translation System for the ACL-WMT 2010
Jan Niehues | Teresa Herrmann | Mohammed Mediani | Alex Waibel
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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The KIT translation system for IWSLT 2010
Jan Niehues | Mohammed Mediani | Teresa Herrmann | Michael Heck | Christian Herff | Alex Waibel
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

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Real-time spoken language identification and recognition for speech-to-speech translation
Daniel Chung Yong Lim | Ian Lane | Alex Waibel
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

2009

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End-to-End Evaluation in Simultaneous Translation
Olivier Hamon | Christian Fügen | Djamel Mostefa | Victoria Arranz | Muntsin Kolss | Alex Waibel | Khalid Choukri
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Incremental Adaptation of Speech-to-Speech Translation
Nguyen Bach | Roger Hsiao | Matthias Eck | Paisarn Charoenpornsawat | Stephan Vogel | Tanja Schultz | Ian Lane | Alex Waibel | Alan Black
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

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The Universität Karlsruhe Translation System for the EACL-WMT 2009
Jan Niehues | Teresa Herrmann | Muntsin Kolss | Alex Waibel
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

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Simultaneous German-English lecture translation.
Muntsin Kolss | Matthias Wölfel | Florian Kraft | Jan Niehues | Matthias Paulik | Alex Waibel
Proceedings of the 5th International Workshop on Spoken Language Translation: Papers

In an increasingly globalized world, situations in which people of different native tongues have to communicate with each other become more and more frequent. In many such situations, human interpreters are prohibitively expensive or simply not available. Automatic spoken language translation (SLT), as a cost-effective solution to this dilemma, has received increased attention in recent years. For a broad number of applications, including live SLT of lectures and oral presentations, these automatic systems should ideally operate in real time and with low latency. Large and highly specialized vocabularies as well as strong variations in speaking style – ranging from read speech to free presentations suffering from spontaneous events – make simultaneous SLT of lectures a challenging task. This paper presents our progress in building a simultaneous German-English lecture translation system. We emphasize some of the challenges which are particular to this language pair and propose solutions to tackle some of the problems encountered.

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Communicating Unknown Words in Machine Translation
Matthias Eck | Stephan Vogel | Alex Waibel
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

A new approach to handle unknown words in machine translation is presented. The basic idea is to find definitions for the unknown words on the source language side and translate those definitions instead. Only monolingual resources are required, which generally offer a broader coverage than bilingual resources and are available for a large number of languages. In order to use this in a machine translation system definitions are extracted automatically from online dictionaries and encyclopedias. The translated definition is then inserted and clearly marked in the original hypothesis. This is shown to lead to significant improvements in (subjective) translation quality.

2007

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Translation Model Pruning via Usage Statistics for Statistical Machine Translation
Matthias Eck | Stephan Vogel | Alex Waibel
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers

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Estimating phrase pair relevance for translation model pruning
Matthias Eck | Stephan Vogel | Alex Waibel
Proceedings of Machine Translation Summit XI: Papers

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The CMU TransTac 2007 eyes-free two-way speech-to-speech translation system
Nguyen Bach | Matthais Eck | Paisarn Charoenpornsawat | Thilo Köhler | Sebastian Stüker | ThuyLinh Nguyen | Roger Hsiao | Alex Waibel | Stephan Vogel | Tanja Schultz | Alan W. Black
Proceedings of the Fourth International Workshop on Spoken Language Translation

The paper describes our portable two-way speech-to-speech translation system using a completely eyes-free/hands-free user interface. This system translates between the language pair English and Iraqi Arabic as well as between English and Farsi, and was built within the framework of the DARPA TransTac program. The Farsi language support was developed within a 90-day period, testing our ability to rapidly support new languages. The paper gives an overview of the system’s components along with the individual component objective measures and a discussion of issues relevant for the overall usage of the system. We found that usability, flexibility, and robustness serve as severe constraints on system architecture and design.

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The CMU-UKA statistical machine translation systems for IWSLT 2007
Ian Lane | Andreas Zollmann | Thuy Linh Nguyen | Nguyen Bach | Ashish Venugopal | Stephan Vogel | Kay Rottmann | Ying Zhang | Alex Waibel
Proceedings of the Fourth International Workshop on Spoken Language Translation

This paper describes the CMU-UKA statistical machine translation systems submitted to the IWSLT 2007 evaluation campaign. Systems were submitted for three language-pairs: Japanese→English, Chinese→English and Arabic→English. All systems were based on a common phrase-based SMT (statistical machine translation) framework but for each language-pair a specific research problem was tackled. For Japanese→English we focused on two problems: first, punctuation recovery, and second, how to incorporate topic-knowledge into the translation framework. Our Chinese→English submission focused on syntax-augmented SMT and for the Arabic→English task we focused on incorporating morphological-decomposition into the SMT framework. This research strategy enabled us to evaluate a wide variety of approaches which proved effective for the language pairs they were evaluated on.

2006

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A Flexible Online Server for Machine Translation Evaluation
Matthias Eck | Stephan Vogel | Alex Waibel
Proceedings of the 11th Annual conference of the European Association for Machine Translation

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The UKA/CMU statistical machine translation system for IWSLT 2006
Matthias Eck | Ian Lane | Nguyen Bach | Sanjika Hewavitharana | Muntsin Kolss | Bing Zhao | Almut Silja Hildebrand | Stephan Vogel | Alex Waibel
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

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The CMU-UKA syntax augmented machine translation system for IWSLT-06
Andreas Zollmann | Ashish Venugopal | Stephan Vogel | Alex Waibel
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

2005

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The CMU Statistical Machine Translation System for IWSLT2005
Sanjika Hewavitharana | Bing Zhao | Hildebrand | Almut Silja | Matthias Eck | Chiori Hori | Stephan Vogel | Alex Waibel
Proceedings of the Second International Workshop on Spoken Language Translation

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Low Cost Portability for Statistical Machine Translation based on N-gram Frequency and TF-IDF
Matthias Eck | Stephan Vogel | Alex Waibel
Proceedings of the Second International Workshop on Spoken Language Translation

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Low Cost Portability for Statistical Machine Translation based on N-gram Coverage
Matthias Eck | Stephan Vogel | Alex Waibel
Proceedings of Machine Translation Summit X: Papers

Statistical machine translation relies heavily on the available training data. However, in some cases, it is necessary to limit the amount of training data that can be created for or actually used by the systems. To solve that problem, we introduce a weighting scheme that tries to select more informative sentences first. This selection is based on the previously unseen n-grams the sentences contain, and it allows us to sort the sentences according to their estimated importance. After sorting, we can construct smaller training corpora, and we are able to demonstrate that systems trained on much less training data show a very competitive performance compared to baseline systems using all available training data.

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Bilingual Word Spectral Clustering for Statistical Machine Translation
Bing Zhao | Eric P. Xing | Alex Waibel
Proceedings of the ACL Workshop on Building and Using Parallel Texts

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Training and Evaluating Error Minimization Decision Rules for Statistical Machine Translation
Ashish Venugopal | Andreas Zollmann | Alex Waibel
Proceedings of the ACL Workshop on Building and Using Parallel Texts

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Augmenting a statistical translation system with a translation memory
Sanjika Hewavitharana | Stephan Vogel | Alex Waibel
Proceedings of the 10th EAMT Conference: Practical applications of machine translation

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Adaptation of the translation model for statistical machine translation based on information retrieval
Almut Silja Hildebrand | Matthias Eck | Stephan Vogel | Alex Waibel
Proceedings of the 10th EAMT Conference: Practical applications of machine translation

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Learning a Log-Linear Model with Bilingual Phrase-Pair Features for Statistical Machine Translation
Bing Zhao | Alex Waibel
Proceedings of the Fourth SIGHAN Workshop on Chinese Language Processing

2004

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Phrase Pair Rescoring with Term Weighting for Statistical Machine Translation
Bing Zhao | Stephan Vogel | Matthias Eck | Alex Waibel
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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Improving Named Entity Translation Combining Phonetic and Semantic Similarities
Fei Huang | Stephan Vogel | Alex Waibel
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

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A Thai Speech Translation System for Medical Dialogs
Tanja Schultz | Dorcas Alexander | Alan W. Black | Kay Peterson | Sinaporn Suebvisai | Alex Waibel
Demonstration Papers at HLT-NAACL 2004

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Language Model Adaptation for Statistical Machine Translation Based on Information Retrieval
Matthias Eck | Stephan Vogel | Alex Waibel
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Interpreting BLEU/NIST Scores: How Much Improvement do We Need to Have a Better System?
Ying Zhang | Stephan Vogel | Alex Waibel
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Improving Statistical Machine Translation in the Medical Domain using the Unified Medical Language system
Matthias Eck | Stephan Vogel | Alex Waibel
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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The ISL EDTRL system
Juergen Reichert | Alex Waibel
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

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The ISL statistical translation system for spoken language translation
Stephan Vogel | Sanjika Hewavitharana | Muntsin Kolss | Alex Waibel
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

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Toward named entity extraction and translation in spoken language translation
Fei Huang | Stephan Vogel | Alex Waibel
Proceedings of the First International Workshop on Spoken Language Translation: Papers

2003

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The CMU statistical machine translation system
Stephan Vogel | Ying Zhang | Fei Huang | Alicia Tribble | Ashish Venugopal | Bing Zhao | Alex Waibel
Proceedings of Machine Translation Summit IX: Papers

In this paper we describe the components of our statistical machine translation system. This system combines phrase-to-phrase translations extracted from a bilingual corpus using different alignment approaches. Special methods to extract and align named entities are used. We show how a manual lexicon can be incorporated into the statistical system in an optimized way. Experiments on Chinese-to-English and Arabic-to-English translation tasks are presented.

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Speechalator: Two-Way Speech-to-Speech Translation in Your Hand
Alex Waibel | Ahmed Badran | Alan W. Black | Robert Frederking | Donna Gates | Alon Lavie | Lori Levin | Kevin Lenzo | Laura Mayfield Tomokiyo | Juergen Reichert | Tanja Schultz | Dorcas Wallace | Monika Woszczyna | Jing Zhang
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations

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Effective Phrase Translation Extraction from Alignment Models
Ashish Venugopal | Stephan Vogel | Alex Waibel
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Efficient Optimization for Bilingual Sentence Alignment Based on Linear Regression
Bing Zhao | Klaus Zechner | Stephen Vogel | Alex Waibel
Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond

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Automatic Extraction of Named Entity Translingual Equivalence Based on Multi-Feature Cost Minimization
Fei Huang | Stephan Vogel | Alex Waibel
Proceedings of the ACL 2003 Workshop on Multilingual and Mixed-language Named Entity Recognition

2002

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Improvements in Non-Verbal Cue Identification Using Multilingual Phone Strings
Tanja Schultz | Qin Jin | Kornel Laskowski | Alicia Tribble | Alex Waibel
Proceedings of the ACL-02 Workshop on Speech-to-Speech Translation: Algorithms and Systems

2001

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Activity detection for information access to oral communication
Klaus Ries | Alex Waibel
Proceedings of the First International Conference on Human Language Technology Research

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Advances in meeting recognition
Alex Waibel | Hua Yu | Tanja Schultz | Yue Pan | Michael Bett | Martin Westphal | Hagen Soltau | Thomas Schaaf | Florian Metze
Proceedings of the First International Conference on Human Language Technology Research

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Architecture and Design Considerations in NESPOLE!: a Speech Translation System for E-commerce Applications
Alon Lavie | Chad Langley | Alex Waibel | Fabio Pianesi | Gianni Lazzari | Paolo Coletti | Loredana Taddei | Franco Balducci
Proceedings of the First International Conference on Human Language Technology Research

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LingWear: A Mobile Tourist Information System
Christian Fügen | Martin Westphal | Mike Schneider | Tanja Schultz | Alex Waibel
Proceedings of the First International Conference on Human Language Technology Research

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Towards Automatic Sign Translation
Jie Yang | Jiang Gao | Ying Zhang | Alex Waibel
Proceedings of the First International Conference on Human Language Technology Research

2000

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Shallow Discourse Genre Annotation in CallHome Spanish
Klaus Ries | Lori Levin | Liza Valle | Alon Lavie | Alex Waibel
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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DIASUMM: Flexible Summarization of Spontaneous Dialogues in Unrestricted Domains
Klaus Zechner | Alex Waibel
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

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Minimizing Word Error Rate in Textual Summaries of Spoken Language
Klaus Zechner | Alex Waibel
1st Meeting of the North American Chapter of the Association for Computational Linguistics

1999

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Translation systems under the C-STAR framework
Alex Waibel
Proceedings of Machine Translation Summit VII

This talk will review our work on Speech Translation under the recent worldwide C-STAR demonstration. C-STAR is the Consortium for Speech Translation Advanced Research and now includes 6 partners and 20 partner/affiliate laboratories around the world. The work demonstrated concludes the second phase of the consortium, which has focused on translating conversational spontaneous speech as opposed to well formed, well structured text. As such, much of the work has focused on exploiting semantic and pragmatic constraints derived from the task domain and dialog situation to produce an understandable translation. Six partners have connected their respective systems with each other and allowed travel related spoken dialogs to provide communication between each of them. A common Interlingua representation was developed and used between the partners to make this multilingual deployment possible. The systems were also complemented by the introduction of Web based shared workspaces that allow one user in one country to communicate pictures, documents, sounds, tables, etc. to the other over the Web while referring to these documents in the dialog. Some of the partners' systems were also deployed in wearable situations, such as a traveler exploring a foreign city. In this case speech and language technology was installed on a wearable computer with a small hand-held display. It was used to provide language translation as well as human-machine information access for the purpose of navigation (using GPS localization) and tour guidance. This combination of human-machine and human-machine-human dialogs could allow a user explore a foreign environment more effectively by resorting to human-machine and human-human dialogs wherever most appropriate.

1998

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Growing Semantic Grammars
Marsal Gavaldà | Alex Waibel
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Modeling with Structures in Statistical Machine Translation
Ye-Yi Wang | Alex Waibel
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Using Chunk Based Partial Parsing of Spontaneous Speech in Unrestricted Domains for Reducing Word Error Rate in Speech Recognition
Klaus Zechner | Alex Waibel
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

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Growing Semantic Grammars
Marsal Gavalda | Alex Waibel
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Modeling with Structures in Statistical Machine translation
Ye-Yi Wang | Alex Waibel
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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Using Chunk Based Partial Parsing of Spontaneous Speech in Unrestricted Domains for Reducing Word Error Rate in Speech Recognition
Klaus Zechner | Alex Waibel
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

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A modular approach to spoken language translation for large domains
Monika Woszczcyna | Matthew Broadhead | Donna Gates | Marsal Gavaldá | Alon Lavie | Lori Levin | Alex Waibel
Proceedings of the Third Conference of the Association for Machine Translation in the Americas: Technical Papers

The MT engine of the JANUS speech-to-speech translation system is designed around four main principles: 1) an interlingua approach that allows the efficient addition of new languages, 2) the use of semantic grammars that yield low cost high quality translations for limited domains, 3) modular grammars that support easy expansion into new domains, and 4) efficient integration of multiple grammars using multi-domain parse lattices and domain re-scoring. Within the framework of the C-STAR-II speech-to-speech translation effort, these principles are tested against the challenge of providing translation for a number of domains and language pairs with the additional restriction of a common interchange format.

1997

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Decoding Algorithm in Statistical Machine Translation
Ye-Yi Wang | Alex Waibel
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

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Expanding the Domain of a Multi-lingual Speech-to-Speech Translation System
Alon Lavie | Lori Levin | Puming Zhan | Maite Taboada | Donna Gates | Mirella Lapata | Cortis Clark | Matthew Broadhead | Alex Waibel
Spoken Language Translation

1996

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JANUS: multi-lingual translation of spontaneous speech in limited domain
Alon Lavie | Lori Levin | Alex Waibel | Donna Gates | Marsal Gavalda | Laura Mayfield
Conference of the Association for Machine Translation in the Americas

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FeasPar - A Feature Structure Parser Learning to Parse Spoken Language
Finn Dag Buo | Alex Waibel
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

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Multi-lingual Translation of Spontaneously Spoken Language in a Limited Domain
Alon Lavie | Donna Gates | Marsal Gavalda | Laura Mayfield | Alex Waibel | Lori Levin
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

1995

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Translation and interpretation of spontaneous speech
Alex Waibel
Proceedings of Machine Translation Summit V

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Using Context in Machine Translation of Spoken Language
Lori Levin | Oren Glickman | Yan Qu | Carolyn P. Rose | Donna Gates | Alon Lavie | Alex Waibel | Carol Van Ess-Dykema
Proceedings of the Sixth Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1994

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Future Directions
Joseph Pentheroudakis | Jaime Carbonell | Lutz Graunitz | Pierre Isabelle | Chris Montgomery | Alex Waibel
Proceedings of the First Conference of the Association for Machine Translation in the Americas

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Recovering From Parser Failures: A Hybrid Statistical/Symbolic Approach
Carolyn Penstein Rose | Alex Waibel
The Balancing Act: Combining Symbolic and Statistical Approaches to Language

1993

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Machine Translation
Alex Waibel
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Recent Advances in Janus: A Speech Translation System
M. Woszczyna | N. Coccaro | A. Eisele | A. Lavie | A. McNair | T. Polzin | I. Rogina | C. P. Rose | T. Sloboda | M. Tomita | J. Tsutsumi | N. Aoki-Waibel | A. Waibel | W. Ward
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Frequency Estimation of Verb Subcategorization Frames Based on Syntactic and Multidimensional Statistical Analysis
Akira Ushioda | David A. Evans | Ted Gibson | Alex Waibel
Proceedings of the Third International Workshop on Parsing Technologies

We describe a mechanism for automatically estimating frequencies of verb subcategorization frames in a large corpus. A tagged corpus is first partially parsed to identify noun phrases and then a regular grammar is used to estimate the appropriate subcategorization frame for each verb token in the corpus. In an experiment involving the identification of six fixed subcategorization frames, our current system showed more than 80% accuracy. In addition, a new statistical method enables the system to learn patterns of errors based on a set of training samples and substantially improves the accuracy of the frequency estimation.

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The Automatic Acquisition of Frequencies of Verb Subcategorization Frames from Tagged Corpora
Akira Ushioda | David A. Evans | Ted Gibson | Alex Waibel
Acquisition of Lexical Knowledge from Text

1989

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A Connectionist Parser Aimed at Spoken Language
Ajay Jain | Alex Waibel
Proceedings of the First International Workshop on Parsing Technologies

We describe a connectionist model which learns to parse single sentences from sequential word input. A parse in the connectionist network contains information about role assignment, prepositional attachment, relative clause structure, and subordinate clause structure. The trained network displays several interesting types of behavior. These include predictive ability, tolerance to certain corruptions of input word sequences, and some generalization capability. We report on experiments in which a small number of sentence types have been successfully learned by a network. Work is in progress on a larger database. Application of this type of connectionist model to the area of spoken language processing is discussed.
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