Eunah Cho


2021

pdf bib
Personalized Search-based Query Rewrite System for Conversational AI
Eunah Cho | Ziyan Jiang | Jie Hao | Zheng Chen | Saurabh Gupta | Xing Fan | Chenlei Guo
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

Query rewrite (QR) is an emerging component in conversational AI systems, reducing user defect. User defect is caused by various reasons, such as errors in the spoken dialogue system, users’ slips of the tongue or their abridged language. Many of the user defects stem from personalized factors, such as user’s speech pattern, dialect, or preferences. In this work, we propose a personalized search-based QR framework, which focuses on automatic reduction of user defect. We build a personalized index for each user, which encompasses diverse affinity layers to reflect personal preferences for each user in the conversational AI. Our personalized QR system contains retrieval and ranking layers. Supported by user feedback based learning, training our models does not require hand-annotated data. Experiments on personalized test set showed that our personalized QR system is able to correct systematic and user errors by utilizing phonetic and semantic inputs.

2020

pdf bib
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

pdf bib
Data Augmentation using Pre-trained Transformer Models
Varun Kumar | Ashutosh Choudhary | Eunah Cho
Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems

Language model based pre-trained models such as BERT have provided significant gains across different NLP tasks. In this paper, we study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation. Additionally, on three classification benchmarks, pre-trained Seq2Seq model outperforms other data augmentation methods in a low-resource setting. Further, we explore how different pre-trained model based data augmentation differs in-terms of data diversity, and how well such methods preserve the class-label information.

2019

pdf bib
Graph-Based Semi-Supervised Learning for Natural Language Understanding
Zimeng Qiu | Eunah Cho | Xiaochun Ma | William Campbell
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graph-based semi-supervised learning models as well as their inductive variants for NLU. We evaluate the approach’s applicability using publicly available NLU data and models. In order to find similar utterances and construct a graph, we use a paraphrase detection model. Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5%.

pdf bib
Paraphrase Generation for Semi-Supervised Learning in NLU
Eunah Cho | He Xie | William M. Campbell
Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation

Semi-supervised learning is an efficient way to improve performance for natural language processing systems. In this work, we propose Para-SSL, a scheme to generate candidate utterances using paraphrasing and methods from semi-supervised learning. In order to perform paraphrase generation in the context of a dialog system, we automatically extract paraphrase pairs to create a paraphrase corpus. Using this data, we build a paraphrase generation system and perform one-to-many generation, followed by a validation step to select only the utterances with good quality. The paraphrase-based semi-supervised learning is applied to five functionalities in a natural language understanding system. Our proposed method for semi-supervised learning using paraphrase generation does not require user utterances and can be applied prior to releasing a new functionality to a system. Experiments show that we can achieve up to 19% of relative slot error reduction without an access to user utterances, and up to 35% when leveraging live traffic utterances.

2017

pdf bib
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.

pdf bib
Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning
Jan Niehues | Eunah Cho
Proceedings of the Second Conference on Machine Translation

pdf bib
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

2016

pdf bib
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

pdf bib
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

pdf bib
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.

pdf bib
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

2015

pdf bib
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

pdf bib
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

pdf bib
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

pdf bib
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

2014

pdf bib
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.

pdf bib
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.

pdf bib
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

pdf bib
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.

pdf bib
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

pdf bib
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

2013

pdf bib
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

pdf bib
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

pdf bib
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.

pdf bib
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.

2012

pdf bib
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

pdf bib
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.

pdf bib
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

2011

pdf bib
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.