Marcello Federico


2022

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CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formality
Maria Nadejde | Anna Currey | Benjamin Hsu | Xing Niu | Marcello Federico | Georgiana Dinu
Findings of the Association for Computational Linguistics: NAACL 2022

The machine translation (MT) task is typically formulated as that of returning a single translation for an input segment. However, in many cases, multiple different translations are valid and the appropriate translation may depend on the intended target audience, characteristics of the speaker, or even the relationship between speakers. Specific problems arise when dealing with honorifics, particularly translating from English into languages with formality markers. For example, the sentence “Are you sure?” can be translated in German as “Sind Sie sich sicher?” (formal register) or “Bist du dir sicher?” (informal). Using wrong or inconsistent tone may be perceived as inappropriate or jarring for users of certain cultures and demographics. This work addresses the problem of learning to control target language attributes, in this case formality, from a small amount of labeled contrastive data. We introduce an annotated dataset (CoCoA-MT) and an associated evaluation metric for training and evaluating formality-controlled MT models for six diverse target languages. We show that we can train formality-controlled models by fine-tuning on labeled contrastive data, achieving high accuracy (82% in-domain and 73% out-of-domain) while maintaining overall quality.

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Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)
Elizabeth Salesky | Marcello Federico | Marta Costa-jussà
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

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Findings of the IWSLT 2022 Evaluation Campaign
Antonios Anastasopoulos | Loïc Barrault | Luisa Bentivogli | Marcely Zanon Boito | Ondřej Bojar | Roldano Cattoni | Anna Currey | Georgiana Dinu | Kevin Duh | Maha Elbayad | Clara Emmanuel | Yannick Estève | Marcello Federico | Christian Federmann | Souhir Gahbiche | Hongyu Gong | Roman Grundkiewicz | Barry Haddow | Benjamin Hsu | Dávid Javorský | Vĕra Kloudová | Surafel Lakew | Xutai Ma | Prashant Mathur | Paul McNamee | Kenton Murray | Maria Nǎdejde | Satoshi Nakamura | Matteo Negri | Jan Niehues | Xing Niu | John Ortega | Juan Pino | Elizabeth Salesky | Jiatong Shi | Matthias Sperber | Sebastian Stüker | Katsuhito Sudoh | Marco Turchi | Yogesh Virkar | Alexander Waibel | Changhan Wang | Shinji Watanabe
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation. A total of 27 teams participated in at least one of the shared tasks. This paper details, for each shared task, the purpose of the task, the data that were released, the evaluation metrics that were applied, the submissions that were received and the results that were achieved.

2021

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Towards Modeling the Style of Translators in Neural Machine Translation
Yue Wang | Cuong Hoang | Marcello Federico
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

One key ingredient of neural machine translation is the use of large datasets from different domains and resources (e.g. Europarl, TED talks). These datasets contain documents translated by professional translators using different but consistent translation styles. Despite that, the model is usually trained in a way that neither explicitly captures the variety of translation styles present in the data nor translates new data in different and controllable styles. In this work, we investigate methods to augment the state of the art Transformer model with translator information that is available in part of the training data. We show that our style-augmented translation models are able to capture the style variations of translators and to generate translations with different styles on new data. Indeed, the generated variations differ significantly, up to +4.5 BLEU score difference. Despite that, human evaluation confirms that the translations are of the same quality.

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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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A Statistical Extension of Byte-Pair Encoding
David Vilar | Marcello Federico
Proceedings of the 18th International Conference on Spoken Language Translation (IWSLT 2021)

Sub-word segmentation is currently a standard tool for training neural machine translation (MT) systems and other NLP tasks. The goal is to split words (both in the source and target languages) into smaller units which then constitute the input and output vocabularies of the MT system. The aim of reducing the size of the input and output vocabularies is to increase the generalization capabilities of the translation model, enabling the system to translate and generate infrequent and new (unseen) words at inference time by combining previously seen sub-word units. Ideally, we would expect the created units to have some linguistic meaning, so that words are created in a compositional way. However, the most popular word-splitting method, Byte-Pair Encoding (BPE), which originates from the data compression literature, does not include explicit criteria to favor linguistic splittings nor to find the optimal sub-word granularity for the given training data. In this paper, we propose a statistically motivated extension of the BPE algorithm and an effective convergence criterion that avoids the costly experimentation cycle needed to select the best sub-word vocabulary size. Experimental results with morphologically rich languages show that our model achieves nearly-optimal BLEU scores and produces morphologically better word segmentations, which allows to outperform BPE’s generalization in the translation of sentences containing new words, as shown via human evaluation.

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Findings of the WMT Shared Task on Machine Translation Using Terminologies
Md Mahfuz Ibn Alam | Ivana Kvapilíková | Antonios Anastasopoulos | Laurent Besacier | Georgiana Dinu | Marcello Federico | Matthias Gallé | Kweonwoo Jung | Philipp Koehn | Vassilina Nikoulina
Proceedings of the Sixth Conference on Machine Translation

Language domains that require very careful use of terminology are abundant and reflect a significant part of the translation industry. In this work we introduce a benchmark for evaluating the quality and consistency of terminology translation, focusing on the medical (and COVID-19 specifically) domain for five language pairs: English to French, Chinese, Russian, and Korean, as well as Czech to German. We report the descriptions and results of the participating systems, commenting on the need for further research efforts towards both more adequate handling of terminologies as well as towards a proper formulation and evaluation of the task.

2020

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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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From Speech-to-Speech Translation to Automatic Dubbing
Marcello Federico | Robert Enyedi | Roberto Barra-Chicote | Ritwik Giri | Umut Isik | Arvindh Krishnaswamy | Hassan Sawaf
Proceedings of the 17th International Conference on Spoken Language Translation

We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing. Our architecture features neural machine translation generating output of preferred length, prosodic alignment of the translation with the original speech segments, neural text-to-speech with fine tuning of the duration of each utterance, and, finally, audio rendering to enriches text-to-speech output with background noise and reverberation extracted from the original audio. We report and discuss results of a first subjective evaluation of automatic dubbing of excerpts of TED Talks from English into Italian, which measures the perceived naturalness of automatic dubbing and the relative importance of each proposed enhancement.

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Joint Translation and Unit Conversion for End-to-end Localization
Georgiana Dinu | Prashant Mathur | Marcello Federico | Stanislas Lauly | Yaser Al-Onaizan
Proceedings of the 17th International Conference on Spoken Language Translation

A variety of natural language tasks require processing of textual data which contains a mix of natural language and formal languages such as mathematical expressions. In this paper, we take unit conversions as an example and propose a data augmentation technique which lead to models learning both translation and conversion tasks as well as how to adequately switch between them for end-to-end localization.

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Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation
John E. Ortega | Marcello Federico | Constantin Orasan | Maja Popovic
Proceedings of 1st Workshop on Post-Editing in Modern-Day Translation

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TICO-19: the Translation Initiative for COvid-19
Antonios Anastasopoulos | Alessandro Cattelan | Zi-Yi Dou | Marcello Federico | Christian Federmann | Dmitriy Genzel | Franscisco Guzmán | Junjie Hu | Macduff Hughes | Philipp Koehn | Rosie Lazar | Will Lewis | Graham Neubig | Mengmeng Niu | Alp Öktem | Eric Paquin | Grace Tang | Sylwia Tur
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves. To this end, the collaborators forming the Translation Initiative for COvid-19 (TICO-19) have made test and development data available to AI and MT researchers in 35 different languages in order to foster the development of tools and resources for improving access to information about COVID-19 in these languages. In addition to 9 high-resourced, ”pivot” languages, the team is targeting 26 lesser resourced languages, in particular languages of Africa, South Asia and South-East Asia, whose populations may be the most vulnerable to the spread of the virus. The same data is translated into all of the languages represented, meaning that testing or development can be done for any pairing of languages in the set. Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.

2019

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The IWSLT 2019 Evaluation Campaign
Jan Niehues | Rolando Cattoni | Sebastian Stüker | Matteo Negri | Marco Turchi | Thanh-Le Ha | Elizabeth Salesky | Ramon Sanabria | Loic Barrault | Lucia Specia | Marcello Federico
Proceedings of the 16th International Conference on Spoken Language Translation

The IWSLT 2019 evaluation campaign featured three tasks: speech translation of (i) TED talks and (ii) How2 instructional videos from English into German and Portuguese, and (iii) text translation of TED talks from English into Czech. For the first two tasks we encouraged submissions of end- to-end speech-to-text systems, and for the second task participants could also use the video as additional input. We received submissions by 12 research teams. This overview provides detailed descriptions of the data and evaluation conditions of each task and reports results of the participating systems.

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Adapting Multilingual Neural Machine Translation to Unseen Languages
Surafel M. Lakew | Alina Karakanta | Marcello Federico | Matteo Negri | Marco Turchi
Proceedings of the 16th International Conference on Spoken Language Translation

Multilingual Neural Machine Translation (MNMT) for low- resource languages (LRL) can be enhanced by the presence of related high-resource languages (HRL), but the relatedness of HRL usually relies on predefined linguistic assumptions about language similarity. Recently, adapting MNMT to a LRL has shown to greatly improve performance. In this work, we explore the problem of adapting an MNMT model to an unseen LRL using data selection and model adapta- tion. In order to improve NMT for LRL, we employ perplexity to select HRL data that are most similar to the LRL on the basis of language distance. We extensively explore data selection in popular multilingual NMT settings, namely in (zero-shot) translation, and in adaptation from a multilingual pre-trained model, for both directions (LRL↔en). We further show that dynamic adaptation of the model’s vocabulary results in a more favourable segmentation for the LRL in comparison with direct adaptation. Experiments show re- ductions in training time and significant performance gains over LRL baselines, even with zero LRL data (+13.0 BLEU), up to +17.0 BLEU for pre-trained multilingual model dynamic adaptation with related data selection. Our method outperforms current approaches, such as massively multilingual models and data augmentation, on four LRL.

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Controlling the Output Length of Neural Machine Translation
Surafel Melaku Lakew | Mattia Di Gangi | Marcello Federico
Proceedings of the 16th International Conference on Spoken Language Translation

The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted. In particular, if translations have to fit some given layout, quality should not only be measured in terms of adequacy and fluency, but also length. Exemplary cases are the translation of document files, subtitles, and scripts for dubbing, where the output length should ideally be as close as possible to the length of the input text. This pa-per addresses for the first time, to the best of our knowledge, the problem of controlling the output length in NMT. We investigate two methods for biasing the output length with a transformer architecture: i) conditioning the output to a given target-source length-ratio class and ii) enriching the transformer positional embedding with length information. Our experiments show that both methods can induce the network to generate shorter translations, as well as acquiring inter- pretable linguistic skills.

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Robust Neural Machine Translation for Clean and Noisy Speech Transcripts
Matti Di Gangi | Robert Enyedi | Alessandra Brusadin | Marcello Federico
Proceedings of the 16th International Conference on Spoken Language Translation

Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is not met in spoken language translation, where the input is generated by an automatic speech recognition (ASR) system. In this paper, we study how to adapt a strong NMT system to make it robust to typical ASR errors. As in our application scenarios transcripts might be post-edited by human experts, we propose adaptation strategies to train a single system that can translate either clean or noisy input with no supervision on the input type. Our experimental results on a public speech translation data set show that adapting a model on a significant amount of parallel data including ASR transcripts is beneficial with test data of the same type, but produces a small degradation when translating clean text. Adapting on both clean and noisy variants of the same data leads to the best results on both input types.

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On the Importance of Word Boundaries in Character-level Neural Machine Translation
Duygu Ataman | Orhan Firat | Mattia A. Di Gangi | Marcello Federico | Alexandra Birch
Proceedings of the 3rd Workshop on Neural Generation and Translation

Neural Machine Translation (NMT) models generally perform translation using a fixed-size lexical vocabulary, which is an important bottleneck on their generalization capability and overall translation quality. The standard approach to overcome this limitation is to segment words into subword units, typically using some external tools with arbitrary heuristics, resulting in vocabulary units not optimized for the translation task. Recent studies have shown that the same approach can be extended to perform NMT directly at the level of characters, which can deliver translation accuracy on-par with subword-based models, on the other hand, this requires relatively deeper networks. In this paper, we propose a more computationally-efficient solution for character-level NMT which implements a hierarchical decoding architecture where translations are subsequently generated at the level of words and characters. We evaluate different methods for open-vocabulary NMT in the machine translation task from English into five languages with distinct morphological typology, and show that the hierarchical decoding model can reach higher translation accuracy than the subword-level NMT model using significantly fewer parameters, while demonstrating better capacity in learning longer-distance contextual and grammatical dependencies than the standard character-level NMT model.

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Training Neural Machine Translation to Apply Terminology Constraints
Georgiana Dinu | Prashant Mathur | Marcello Federico | Yaser Al-Onaizan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algorithm in order to constrain the output to include run-time-provided target terms. While being effective, these constrained decoding methods add, however, significant computational overhead to the inference step, and, as we show in this paper, can be brittle when tested in realistic conditions. In this paper we approach the problem by training a neural MT system to learn how to use custom terminology when provided with the input. Comparative experiments show that our method is not only more effective than a state-of-the-art implementation of constrained decoding, but is also as fast as constraint-free decoding.

2018

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An Evaluation of Two Vocabulary Reduction Methods for Neural Machine Translation
Duygu Ataman | Marcello Federico
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Challenges in Adaptive Neural Machine Translation
Marcello Federico
Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing

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Neural Machine Translation into Language Varieties
Surafel Melaku Lakew | Aliia Erofeeva | Marcello Federico
Proceedings of the Third Conference on Machine Translation: Research Papers

Both research and commercial machine translation have so far neglected the importance of properly handling the spelling, lexical and grammar divergences occurring among language varieties. Notable cases are standard national varieties such as Brazilian and European Portuguese, and Canadian and European French, which popular online machine translation services are not keeping distinct. We show that an evident side effect of modeling such varieties as unique classes is the generation of inconsistent translations. In this work, we investigate the problem of training neural machine translation from English to specific pairs of language varieties, assuming both labeled and unlabeled parallel texts, and low-resource conditions. We report experiments from English to two pairs of dialects, European-Brazilian Portuguese and European-Canadian French, and two pairs of standardized varieties, Croatian-Serbian and Indonesian-Malay. We show significant BLEU score improvements over baseline systems when translation into similar languages is learned as a multilingual task with shared representations.

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Compositional Representation of Morphologically-Rich Input for Neural Machine Translation
Duygu Ataman | Marcello Federico
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Neural machine translation (NMT) models are typically trained with fixed-size input and output vocabularies, which creates an important bottleneck on their accuracy and generalization capability. As a solution, various studies proposed segmenting words into sub-word units and performing translation at the sub-lexical level. However, statistical word segmentation methods have recently shown to be prone to morphological errors, which can lead to inaccurate translations. In this paper, we propose to overcome this problem by replacing the source-language embedding layer of NMT with a bi-directional recurrent neural network that generates compositional representations of the input at any desired level of granularity. We test our approach in a low-resource setting with five languages from different morphological typologies, and under different composition assumptions. By training NMT to compose word representations from character n-grams, our approach consistently outperforms (from 1.71 to 2.48 BLEU points) NMT learning embeddings of statistically generated sub-word units.

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A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation
Surafel Melaku Lakew | Mauro Cettolo | Marcello Federico
Proceedings of the 27th International Conference on Computational Linguistics

Recently, neural machine translation (NMT) has been extended to multilinguality, that is to handle more than one translation direction with a single system. Multilingual NMT showed competitive performance against pure bilingual systems. Notably, in low-resource settings, it proved to work effectively and efficiently, thanks to shared representation space that is forced across languages and induces a sort of transfer-learning. Furthermore, multilingual NMT enables so-called zero-shot inference across language pairs never seen at training time. Despite the increasing interest in this framework, an in-depth analysis of what a multilingual NMT model is capable of and what it is not is still missing. Motivated by this, our work (i) provides a quantitative and comparative analysis of the translations produced by bilingual, multilingual and zero-shot systems; (ii) investigates the translation quality of two of the currently dominant neural architectures in MT, which are the Recurrent and the Transformer ones; and (iii) quantitatively explores how the closeness between languages influences the zero-shot translation. Our analysis leverages multiple professional post-edits of automatic translations by several different systems and focuses both on automatic standard metrics (BLEU and TER) and on widely used error categories, which are lexical, morphology, and word order errors.

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The IWSLT 2018 Evaluation Campaign
Jan Niehues | Rolando Cattoni | Sebastian Stüker | Mauro Cettolo | Marco Turchi | Marcello Federico
Proceedings of the 15th International Conference on Spoken Language Translation

The International Workshop of Spoken Language Translation (IWSLT) 2018 Evaluation Campaign featured two tasks: low-resource machine translation and speech translation. In the first task, manually transcribed speech had to be translated from Basque to English. Since this translation direction is a under-resourced language pair, participants were encouraged to use additional parallel data from related languages. In the second task, participants had to translate English audio into German text with a full speech-translation system. In the baseline condition, participants were free to use composite architectures, while in the end-to-end condition they were restricted to use a single model for the task. This year, eight research groups took part in the low-resource machine translation task and nine in the speech translation task.

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Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
Surafel M. Lakew | Aliia Erofeeva | Matteo Negri | Marcello Federico | Marco Turchi
Proceedings of the 15th International Conference on Spoken Language Translation

We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i.e., introducing new vocabulary items if they are not included in the initial model). The parameter transfer mechanism is evaluated in two scenarios: i) to adapt a trained single language NMT system to work with a new language pair and ii) to continuously add new language pairs to grow to a multilingual NMT system. In both the scenarios our goal is to improve the translation performance, while minimizing the training convergence time. Preliminary experiments spanning five languages with different training data sizes (i.e., 5k and 50k parallel sentences) show a significant performance gain ranging from +3.85 up to +13.63 BLEU in different language directions. Moreover, when compared with training an NMT model from scratch, our transfer-learning approach allows us to reach higher performance after training up to 4% of the total training steps.

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Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment
Luisa Bentivogli | Mauro Cettolo | Marcello Federico | Christian Federmann
Proceedings of the 15th International Conference on Spoken Language Translation

In this paper we present an analysis of the two most prominent methodologies used for the human evaluation of MT quality, namely evaluation based on Post-Editing (PE) and evaluation based on Direct Assessment (DA). To this purpose, we exploit a publicly available large dataset containing both types of evaluations. We first focus on PE and investigate how sensitive TER-based evaluation is to the type and number of references used. Then, we carry out a comparative analysis of PE and DA to investigate the extent to which the evaluation results obtained by methodologies addressing different human perspectives are similar. This comparison sheds light not only on PE but also on the so-called reference bias related to monolingual DA. Also, we analyze if and how the two methodologies can complement each other’s weaknesses.

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Adapting Multilingual NMT to Extremely Low Resource Languages FBK’s Participation in the Basque-English Low-Resource MT Task, IWSLT 2018
Surafel M. Lakew | Marcello Federico
Proceedings of the 15th International Conference on Spoken Language Translation

Multilingual neural machine translation (M-NMT) has recently shown to improve performance of machine translation of low-resource languages. Thanks to its implicit transfer-learning mechanism, the availability of a highly resourced language pair can be leveraged to learn useful representation for a lower resourced language. This work investigates how a low-resource translation task can be improved within a multilingual setting. First, we adapt a system trained on multiple language directions to a specific language pair. Then, we utilize the adapted model to apply an iterative training-inference scheme [1] using monolingual data. In the experimental setting, an extremely low-resourced Basque-English language pair (i.e., ≈ 5.6K in-domain training data) is our target translation task, where we considered a closely related French/Spanish-English parallel data to build the multilingual model. Experimental results from an i) in-domain and ii) an out-of-domain setting with additional training data, show improvements with our approach. We report a translation performance of 15.89 with the former and 23.99 BLEU with the latter on the official IWSLT 2018 Basque-English test set.

2017

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Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario
M. Amin Farajian | Marco Turchi | Matteo Negri | Nicola Bertoldi | Marcello Federico
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

State-of-the-art neural machine translation (NMT) systems are generally trained on specific domains by carefully selecting the training sets and applying proper domain adaptation techniques. In this paper we consider the real world scenario in which the target domain is not predefined, hence the system should be able to translate text from multiple domains. We compare the performance of a generic NMT system and phrase-based statistical machine translation (PBMT) system by training them on a generic parallel corpus composed of data from different domains. Our results on multi-domain English-French data show that, in these realistic conditions, PBMT outperforms its neural counterpart. This raises the question: is NMT ready for deployment as a generic/multi-purpose MT backbone in real-world settings?

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Multi-Domain Neural Machine Translation through Unsupervised Adaptation
M. Amin Farajian | Marco Turchi | Matteo Negri | Marcello Federico
Proceedings of the Second Conference on Machine Translation

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Guiding Neural Machine Translation Decoding with External Knowledge
Rajen Chatterjee | Matteo Negri | Marco Turchi | Marcello Federico | Lucia Specia | Frédéric Blain
Proceedings of the Second Conference on Machine Translation

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FBK’s Participation to the English-to-German News Translation Task of WMT 2017
Mattia Antonino Di Gangi | Nicola Bertoldi | Marcello Federico
Proceedings of the Second Conference on Machine Translation

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Overview of the IWSLT 2017 Evaluation Campaign
Mauro Cettolo | Marcello Federico | Luisa Bentivogli | Jan Niehues | Sebastian Stüker | Katsuhito Sudoh | Koichiro Yoshino | Christian Federmann
Proceedings of the 14th International Conference on Spoken Language Translation

The IWSLT 2017 evaluation campaign has organised three tasks. The Multilingual task, which is about training machine translation systems handling many-to-many language directions, including so-called zero-shot directions. The Dialogue task, which calls for the integration of context information in machine translation, in order to resolve anaphoric references that typically occur in human-human dialogue turns. And, finally, the Lecture task, which offers the challenge of automatically transcribing and translating real-life university lectures. Following the tradition of these reports, we will described all tasks in detail and present the results of all runs submitted by their participants.

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FBK’s Multilingual Neural Machine Translation System for IWSLT 2017
Surafel M. Lakew | Quintino F. Lotito | Marco Turchi | Matteo Negri | Marcello Federico
Proceedings of the 14th International Conference on Spoken Language Translation

Neural Machine Translation has been shown to enable inference and cross-lingual knowledge transfer across multiple language directions using a single multilingual model. Focusing on this multilingual translation scenario, this work summarizes FBK’s participation in the IWSLT 2017 shared task. Our submissions rely on two multilingual systems trained on five languages (English, Dutch, German, Italian, and Romanian). The first one is a 20 language direction model, which handles all possible combinations of the five languages. The second multilingual system is trained only on 16 directions, leaving the others as zero-shot translation directions (i.e representing a more complex inference task on language pairs not seen at training time). More specifically, our zero-shot directions are Dutch$German and Italian$Romanian (resulting in four language combinations). Despite the small amount of parallel data used for training these systems, the resulting multilingual models are effective, even in comparison with models trained separately for every language pair (i.e. in more favorable conditions). We compare and show the results of the two multilingual models against a baseline single language pair systems. Particularly, we focus on the four zero-shot directions and show how a multilingual model trained with small data can provide reasonable results. Furthermore, we investigate how pivoting (i.e using a bridge/pivot language for inference in a source!pivot!target translations) using a multilingual model can be an alternative to enable zero-shot translation in a low resource setting.

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Monolingual Embeddings for Low Resourced Neural Machine Translation
Mattia Antonino Di Gangi | Marcello Federico
Proceedings of the 14th International Conference on Spoken Language Translation

Neural machine translation (NMT) is the state of the art for machine translation, and it shows the best performance when there is a considerable amount of data available. When only little data exist for a language pair, the model cannot produce good representations for words, particularly for rare words. One common solution consists in reducing data sparsity by segmenting words into sub-words, in order to allow rare words to have shared representations with other words. Taking a different approach, in this paper we present a method to feed an NMT network with word embeddings trained on monolingual data, which are combined with the task-specific embeddings learned at training time. This method can leverage an embedding matrix with a huge number of words, which can therefore extend the word-level vocabulary. Our experiments on two language pairs show good results for the typical low-resourced data scenario (IWSLT in-domain dataset). Our consistent improvements over the baselines represent a positive proof about the possibility to leverage models pre-trained on monolingual data in NMT.

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Improving Zero-Shot Translation of Low-Resource Languages
Surafel M. Lakew | Quintino F. Lotito | Matteo Negri | Marco Turchi | Marcello Federico
Proceedings of the 14th International Conference on Spoken Language Translation

Recent work on multilingual neural machine translation reported competitive performance with respect to bilingual models and surprisingly good performance even on (zero-shot) translation directions not observed at training time. We investigate here a zero-shot translation in a particularly low-resource multilingual setting. We propose a simple iterative training procedure that leverages a duality of translations directly generated by the system for the zero-shot directions. The translations produced by the system (sub-optimal since they contain mixed language from the shared vocabulary), are then used together with the original parallel data to feed and iteratively re-train the multilingual network. Over time, this allows the system to learn from its own generated and increasingly better output. Our approach shows to be effective in improving the two zero-shot directions of our multilingual model. In particular, we observed gains of about 9 BLEU points over a baseline multilingual model and up to 2.08 BLEU over a pivoting mechanism using two bilingual models. Further analysis shows that there is also a slight improvement in the non-zero-shot language directions.

2016

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Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts
Marcello Federico | Akiko Aizawa
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts

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The IWSLT 2016 Evaluation Campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Rolando Cattoni | Marcello Federico
Proceedings of the 13th International Conference on Spoken Language Translation

The IWSLT 2016 Evaluation Campaign featured two tasks: the translation of talks and the translation of video conference conversations. While the first task extends previously offered tasks with talks from a different source, the second task is completely new. For both tasks, three tracks were organised: automatic speech recognition (ASR), spoken language translation (SLT), and machine translation (MT). Main translation directions that were offered are English to/from German and English to French. Additionally, the MT track included English to/from Arabic and Czech, as well as French to English. We received this year run submissions from 11 research labs. All runs were evaluated with objective metrics, while submissions for two of the MT talk tasks were also evaluated with human post-editing. Results of the human evaluation show improvements over the best submissions of last year.

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FBK’s Neural Machine Translation Systems for IWSLT 2016
M. Amin Farajian | Rajen Chatterjee | Costanza Conforti | Shahab Jalalvand | Vevake Balaraman | Mattia A. Di Gangi | Duygu Ataman | Marco Turchi | Matteo Negri | Marcello Federico
Proceedings of the 13th International Conference on Spoken Language Translation

In this paper, we describe FBK’s neural machine translation (NMT) systems submitted at the International Workshop on Spoken Language Translation (IWSLT) 2016. The systems are based on the state-of-the-art NMT architecture that is equipped with a bi-directional encoder and an attention mechanism in the decoder. They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words. We compare performances of word and subword NMT systems along with different optimizers. Further, we explore different ensemble techniques to leverage multiple models within the same and across different networks. Several reranking methods are also explored. Our submissions cover all directions of the MSLT task, as well as en-{de, fr} and {de, fr}-en directions of TED. Compared to previously published best results on the TED 2014 test set, our models achieve comparable results on en-de and surpass them on en-fr (+2 BLEU) and fr-en (+7.7 BLEU) language pairs.

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WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words
Luisa Bentivogli | Mauro Cettolo | M. Amin Farajian | Marcello Federico
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper presents WAGS (Word Alignment Gold Standard), a novel benchmark which allows extensive evaluation of WA tools on out-of-vocabulary (OOV) and rare words. WAGS is a subset of the Common Test section of the Europarl English-Italian parallel corpus, and is specifically tailored to OOV and rare words. WAGS is composed of 6,715 sentence pairs containing 11,958 occurrences of OOV and rare words up to frequency 15 in the Europarl Training set (5,080 English words and 6,878 Italian words), representing almost 3% of the whole text. Since WAGS is focused on OOV/rare words, manual alignments are provided for these words only, and not for the whole sentences. Two off-the-shelf word aligners have been evaluated on WAGS, and results have been compared to those obtained on an existing benchmark tailored to full text alignment. The results obtained confirm that WAGS is a valuable resource, which allows a statistically sound evaluation of WA systems’ performance on OOV and rare words, as well as extensive data analyses. WAGS is publicly released under a Creative Commons Attribution license.

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TMop: a Tool for Unsupervised Translation Memory Cleaning
Masoud Jalili Sabet | Matteo Negri | Marco Turchi | José G. C. de Souza | Marcello Federico
Proceedings of ACL-2016 System Demonstrations

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Neural versus Phrase-Based Machine Translation Quality: a Case Study
Luisa Bentivogli | Arianna Bisazza | Mauro Cettolo | Marcello Federico
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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MT Adaptation from TMs in ModernMT
Marcello Federico
Conferences of the Association for Machine Translation in the Americas: MT Users' Track

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Surveys: A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Arianna Bisazza | Marcello Federico
Computational Linguistics, Volume 42, Issue 2 - June 2016

2015

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Topic adaptation for machine translation of e-commerce content
Prashant Mathur | Marcello Federico | Selçuk Köprü | Sharam Khadivi | Hassan Sawaf
Proceedings of Machine Translation Summit XV: Papers

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MT quality estimation for e-commerce data
José G. C. de Souza | Marcello Federico | Hassan Sawaf
Proceedings of Machine Translation Summit XV: User Track

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The IWSLT 2015 Evaluation Campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Roldano Cattoni | Marcello Federico
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign

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MT Quality Estimation for Computer-assisted Translation: Does it Really Help?
Marco Turchi | Matteo Negri | Marcello Federico
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Online Word Alignment for Online Adaptive Machine Translation
M. Amin Farajian | Nicola Bertoldi | Marcello Federico
Proceedings of the EACL 2014 Workshop on Humans and Computer-assisted Translation

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Assessing the Impact of Translation Errors on Machine Translation Quality with Mixed-effects Models
Marcello Federico | Matteo Negri | Luisa Bentivogli | Marco Turchi
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign
Marcello Federico | Sebastian Stüker | François Yvon
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

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Report on the 11th IWSLT evaluation campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Marcello Federico
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

The paper overviews the 11th evaluation campaign organized by the IWSLT workshop. The 2014 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included three automatic speech recognition tracks, on English, German and Italian, five speech translation tracks, from English to French, English to German, German to English, English to Italian, and Italian to English, and five text translation track, also from English to French, English to German, German to English, English to Italian, and Italian to English. In addition to the official tracks, speech and text translation optional tracks were offered, globally involving 12 other languages: Arabic, Spanish, Portuguese (B), Hebrew, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 21 teams participated in the evaluation, for a total of 76 primary runs submitted. Participants were also asked to submit runs on the 2013 test set (progress test set), in order to measure the progress of systems with respect to the previous year. All runs were evaluated with objective metrics, and submissions for two of the official text translation tracks were also evaluated with human post-editing.

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FBK’s machine translation and speech translation systems for the IWSLT 2014 evaluation campaign
Nicola Bertoldi | Prashanu Mathur | Nicolas Ruiz | Marcello Federico
Proceedings of the 11th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the systems submitted by FBK for the MT and SLT tracks of IWSLT 2014. We participated in the English-French and German-English machine translation tasks, as well as the English-French speech translation task. We report improvements in our English-French MT systems over last year’s baselines, largely due to improved techniques of combining translation and language models, and using huge language models. For our German-English system, we experimented with a novel domain adaptation technique. For both language pairs we also applied a novel word triggerbased model which shows slight improvements on EnglishFrench and German-English systems. Our English-French SLT system utilizes MT-based punctuation insertion, recasing, and ASR-like synthesized MT training data.

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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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Online multi-user adaptive statistical machine translation
Prashant Mathur | Mauro Cettolo | Marcello Federico | José G.C. de Souza
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

In this paper we investigate the problem of adapting a machine translation system to the feedback provided by multiple post-editors. It is well know that translators might have very different post-editing styles and that this variability hinders the application of online learning methods, which indeed assume a homogeneous source of adaptation data. We hence propose multi-task learning to leverage bias information from each single post-editors in order to constrain the evolution of the SMT system. A new framework for significance testing with sentence level metrics is described which shows that Multi-Task learning approaches outperforms existing online learning approaches, with significant gains of 1.24 and 1.88 TER score over a strong online adaptive baseline, on a test set of post-edits produced by four translators texts and on a popular benchmark with multiple references, respectively.

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The repetition rate of text as a predictor of the effectiveness of machine translation adaptation
Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

Since the effectiveness of MT adaptation relies on the text repetitiveness, the question on how to measure repetitions in a text naturally arises. This work deals with the issue of looking for and evaluating text features that might help the prediction of the impact of MT adaptation on translation quality. In particular, the repetition rate metric, we recently proposed, is compared to other features employed in very related NLP tasks. The comparison is carried out through a regression analysis between feature values and MT performance gains by dynamically adapted versus non-adapted MT engines, on five different translation tasks. The main outcome of experiments is that the repetition rate correlates better than any other considered feature with the MT gains yielded by the online adaptation, although using all features jointly results in better predictions than with any single feature.

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Assessing the impact of speech recognition errors on machine translation quality
Nicholas Ruiz | Marcello Federico
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track

In spoken language translation, it is crucial that an automatic speech recognition (ASR) system produces outputs that can be adequately translated by a statistical machine translation (SMT) system. While word error rate (WER) is the standard metric of ASR quality, the assumption that each ASR error type is weighted equally is violated in a SMT system that relies on structured input. In this paper, we outline a statistical framework for analyzing the impact of specific ASR error types on translation quality in a speech translation pipeline. Our approach is based on linear mixed-effects models, which allow the analysis of ASR errors on a translation quality metric. The mixed-effects models take into account the variability of ASR systems and the difficulty of each speech utterance being translated in a specific experimental setting. We use mixed-effects models to verify that the ASR errors that compose the WER metric do not contribute equally to translation quality and that interactions exist between ASR errors that cumulatively affect a SMT system’s ability to translate an utterance. Our experiments are carried out on the English to French language pair using eight ASR systems and seven post-edited machine translation references from the IWSLT 2013 evaluation campaign. We report significant findings that demonstrate differences in the contributions of specific ASR error types toward speech translation quality and suggest further error types that may contribute to translation difficulty.

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MateCat: an open source CAT tool for MT post-editing
Marcello Federico | Nicola Bertoldi | Marco Trombetti | Alessandro Cattelan
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Tutorials

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Working with MateCat: user manual and installation guide
Marcello Federico | Nicola Bertoldi | Marco Trombetti | Alessandro Cattelan
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: Tutorials

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Proceedings of the 17th Annual conference of the European Association for Machine Translation
Mauro Cettolo | Marcello Federico | Lucia Specia | Andy Way
Proceedings of the 17th Annual conference of the European Association for Machine Translation

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MateCat
Marcello Federico
Proceedings of the 17th Annual conference of the European Association for Machine Translation

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Complexity of spoken versus written language for machine translation
Nicholas Ruiz | Marcello Federico
Proceedings of the 17th Annual conference of the European Association for Machine Translation

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MT-EQuAl: a Toolkit for Human Assessment of Machine Translation Output
Christian Girardi | Luisa Bentivogli | Mohammad Amin Farajian | Marcello Federico
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

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The MateCat Tool
Marcello Federico | Nicola Bertoldi | Mauro Cettolo | Matteo Negri | Marco Turchi | Marco Trombetti | Alessandro Cattelan | Antonio Farina | Domenico Lupinetti | Andrea Martines | Alberto Massidda | Holger Schwenk | Loïc Barrault | Frederic Blain | Philipp Koehn | Christian Buck | Ulrich Germann
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

2013

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Coping with the Subjectivity of Human Judgements in MT Quality Estimation
Marco Turchi | Matteo Negri | Marcello Federico
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Online Learning Approaches in Computer Assisted Translation
Prashant Mathur | Mauro Cettolo | Marcello Federico
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Efficient Solutions for Word Reordering in German-English Phrase-Based Statistical Machine Translation
Arianna Bisazza | Marcello Federico
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Report on the 10th IWSLT evaluation campaign
Mauro Cettolo | Jan Niehues | Sebastian Stüker | Luisa Bentivogli | Marcello Federico
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

The paper overviews the tenth evaluation campaign organized by the IWSLT workshop. The 2013 evaluation offered multiple tracks on lecture transcription and translation based on the TED Talks corpus. In particular, this year IWSLT included two automatic speech recognition tracks, on English and German, three speech translation tracks, from English to French, English to German, and German to English, and three text translation track, also from English to French, English to German, and German to English. In addition to the official tracks, speech and text translation optional tracks were offered involving 12 other languages: Arabic, Spanish, Portuguese (B), Italian, Chinese, Polish, Persian, Slovenian, Turkish, Dutch, Romanian, Russian. Overall, 18 teams participated in the evaluation for a total of 217 primary runs submitted. All runs were evaluated with objective metrics on a current test set and two progress test sets, in order to compare the progresses against systems of the previous years. In addition, submissions of one of the official machine translation tracks were also evaluated with human post-editing.

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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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FBK’s machine translation systems for the IWSLT 2013 evaluation campaign
Nicola Bertoldi | M. Amin Farajian | Prashant Mathur | Nicholas Ruiz | Marcello Federico
Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the systems submitted by FBK for the MT track of IWSLT 2013. We participated in the English-French as well as the bidirectional Persian-English translation tasks. We report substantial improvements in our English-French systems over last year’s baselines, largely due to improved techniques of combining translation and language models. For our Persian-English and English-Persian systems, we observe substantive improvements over baselines submitted by the workshop organizers, due to enhanced language-specific text normalization and the creation of a large monolingual news corpus in Persian.

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Dynamically Shaping the Reordering Search Space of Phrase-Based Statistical Machine Translation
Arianna Bisazza | Marcello Federico
Transactions of the Association for Computational Linguistics, Volume 1

Defining the reordering search space is a crucial issue in phrase-based SMT between distant languages. In fact, the optimal trade-off between accuracy and complexity of decoding is nowadays reached by harshly limiting the input permutation space. We propose a method to dynamically shape such space and, thus, capture long-range word movements without hurting translation quality nor decoding time. The space defined by loose reordering constraints is dynamically pruned through a binary classifier that predicts whether a given input word should be translated right after another. The integration of this model into a phrase-based decoder improves a strong Arabic-English baseline already including state-of-the-art early distortion cost (Moore and Quirk, 2007) and hierarchical phrase orientation models (Galley and Manning, 2008). Significant improvements in the reordering of verbs are achieved by a system that is notably faster than the baseline, while bleu and meteor remain stable, or even increase, at a very high distortion limit.

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Generative and Discriminative Methods for Online Adaptation in SMT
Katharina Wäschle | Patrick Simianer | Nicola Bertoldi | Stefan Riezler | Marcello Federico
Proceedings of Machine Translation Summit XIV: Papers

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Project Adaptation for MT-Enhanced Computer Assisted Translation
Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of Machine Translation Summit XIV: Papers

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Cache-based Online Adaptation for Machine Translation Enhanced Computer Assisted Translation
Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of Machine Translation Summit XIV: Papers

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Issues in incremental adaptation of statistical MT from human post-edits
Mauro Cettolo | Christophe Servan | Nicola Bertoldi | Marcello Federico | Loïc Barrault | Holger Schwenk
Proceedings of the 2nd Workshop on Post-editing Technology and Practice

2012

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Modified Distortion Matrices for Phrase-Based Statistical Machine Translation
Arianna Bisazza | Marcello Federico
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Detecting Semantic Equivalence and Information Disparity in Cross-lingual Documents
Yashar Mehdad | Matteo Negri | Marcello Federico
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Measuring User Productivity in Machine Translation Enhanced Computer Assisted Translation
Marcello Federico | Alessandro Cattelan | Marco Trombetti
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers

This paper addresses the problem of reliably measuring productivity gains by professional translators working with a machine translation enhanced computer assisted translation tool. In particular, we report on a field test we carried out with a commercial CAT tool in which translation memory matches were supplemented with suggestions from a commercial machine translation engine. The field test was conducted with 12 professional translators working on real translation projects. Productivity of translators were measured with two indicators, post-editing speed and post-editing effort, on two translation directions, English–Italian and English–German, and two linguistic domains, legal and information technology. Besides a detailed statistical analysis of the experimental results, we also discuss issues encountered in running the test.

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Practical Domain Adaptation in SMT
Marcello Federico | Nicola Bertoldi
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Tutorials

Several studies have recently reported significant productivity gains by human translators when besides translation memory (TM) matches they do also receive suggestions from a statistical machine translation (SMT) engine. In fact, an increasing number of language service providers and in-house translation services of large companies is nowadays integrating SMT in their workflow. The technology transfer of state-of-the-art SMT technology from research to industry has been relatively fast and simple also thanks to development of open source software, such as MOSES, GIZA++, and IRSTLM. While a translator is working on a specific translation project, she evaluates the utility of translating versus post-editing a segment based on the adequacy and fluency provided by the SMT engine, which in turn depends on the considered language pair, linguistic domain of the task, and the amount of available training data. Statistical models, like those employed in SMT, rely on a simple assumption: data used to train and tune the models represent the target translation task. Unfortunately, this assumption cannot be satisfied for most of the real application cases, simply because for most of the language pairs and domains there is no sufficient data to adequately train an SMT system. Hence, common practice is to train SMT systems by merging together parallel and monolingual data from the target domain with as much as possible data from any other available source. This workaround is simple and gives practical benefits but is often not the best way to exploit the available data. This tutorial copes with the optimal use of in-domain and out-of-domain data to achieve better SMT performance on a given application domain. Domain adaptation, in general, refers to statistical modeling and machine learning techniques that try to cope with the unavoidable mismatch between training and task data that typically occurs in real life applications. Our tutorial will survey several application cases in which domain adaptation can be applied, and presents adaptation techniques that best fit each case. In particular, we will cover adaptation methods for n-gram language models and translation models in phrase-based SMT. The tutorial will provide some high-level theoretical background in domain adaptation, it will discuss practical application cases, and finally show how the presented methods can be applied with two widely used software tools: Moses and IRSTLM. The tutorial is suited for any practitioner of statistical machine translation. No particular programming or mathematical background is required.

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Match without a Referee: Evaluating MT Adequacy without Reference Translations
Yashar Mehdad | Matteo Negri | Marcello Federico
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Evaluating the Learning Curve of Domain Adaptive Statistical Machine Translation Systems
Nicola Bertoldi | Mauro Cettolo | Marcello Federico | Christian Buck
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Cutting the Long Tail: Hybrid Language Models for Translation Style Adaptation
Arianna Bisazza | Marcello Federico
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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MDI adaptation for the lazy: avoiding normalization in LM adaptation for lecture translation
Nick Ruiz | Marcello Federico
Proceedings of the 9th International Workshop on Spoken Language Translation: Papers

This paper provides a fast alternative to Minimum Discrimination Information-based language model adaptation for statistical machine translation. We provide an alternative to computing a normalization term that requires computing full model probabilities (including back-off probabilities) for all n-grams. Rather than re-estimating an entire language model, our Lazy MDI approach leverages a smoothed unigram ratio between an adaptation text and the background language model to scale only the n-gram probabilities corresponding to translation options gathered by the SMT decoder. The effects of the unigram ratio are scaled by adding an additional feature weight to the log-linear discriminative model. We present results on the IWSLT 2012 TED talk translation task and show that Lazy MDI provides comparable language model adaptation performance to classic MDI.

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Proceedings of the 16th Annual conference of the European Association for Machine Translation
Mauro Cettolo | Marcello Federico | Lucia Specia | Andy Way
Proceedings of the 16th Annual conference of the European Association for Machine Translation

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Crowd-based MT Evaluation for non-English Target Languages
Michael Paul | Eiichiro Sumita | Luisa Bentivogli | Marcello Federico
Proceedings of the 16th Annual conference of the European Association for Machine Translation

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WIT3: Web Inventory of Transcribed and Translated Talks
Mauro Cettolo | Christian Girardi | Marcello Federico
Proceedings of the 16th Annual conference of the European Association for Machine Translation

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The IWSLT 2011 Evaluation Campaign on Automatic Talk Translation
Marcello Federico | Sebastian Stüker | Luisa Bentivogli | Michael Paul | Mauro Cettolo | Teresa Herrmann | Jan Niehues | Giovanni Moretti
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We report here on the eighth evaluation campaign organized in 2011 by the IWSLT workshop series. That IWSLT 2011 evaluation focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination. Unlike in previous years, all data supplied for the evaluation has been publicly released on the workshop website, and is at the disposal of researchers interested in working on our benchmarks and in comparing their results with those published at the workshop. This paper provides an overview of the IWSLT 2011 evaluation campaign, and describes the data supplied, the evaluation infrastructure made available to participants, and the subjective evaluation carried out.

2011

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Using Bilingual Parallel Corpora for Cross-Lingual Textual Entailment
Yashar Mehdad | Matteo Negri | Marcello Federico
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Methods for Smoothing the Optimizer Instability in SMT
Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of Machine Translation Summit XIII: Papers

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Getting Expert Quality from the Crowd for Machine Translation Evaluation
Luisa Bentivogli | Marcello Federico | Giovanni Moretti | Michael Paul
Proceedings of Machine Translation Summit XIII: Papers

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Topic Adaptation for Lecture Translation through Bilingual Latent Semantic Models
Nick Ruiz | Marcello Federico
Proceedings of the Sixth Workshop on Statistical Machine Translation

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The Uppsala-FBK systems at WMT 2011
Christian Hardmeier | Jörg Tiedemann | Markus Saers | Marcello Federico | Prashant Mathur
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Book Review: Cross-Language Information Retrieval by Jian-Yun Nie
Marcello Federico
Computational Linguistics, Volume 37, Issue 2 - June 2011

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Bootstrapping Arabic-Italian SMT through Comparable Texts and Pivot Translation
Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of the 15th Annual conference of the European Association for Machine Translation

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Overview of the IWSLT 2011 evaluation campaign
Marcello Federico | Luisa Bentivogli | Michael Paul | Sebastian Stüker
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

We report here on the eighth Evaluation Campaign organized by the IWSLT workshop. This year, the IWSLT evaluation focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination. Unlike previous years, all data supplied for the evaluation has been publicly released on the workshop website, and is at the disposal of researchers interested in working on our benchmarks and in comparing their results with those published at the workshop. This paper provides an overview of the IWSLT 2011 Evaluation Campaign, which includes: descriptions of the supplied data and evaluation specifications of each track, the list of participants specifying their submitted runs, a detailed description of the subjective evaluation carried out, the main findings of each exercise drawn from the results and the system descriptions prepared by the participants, and, finally, several detailed tables reporting all the evaluation results.

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Fill-up versus interpolation methods for phrase-based SMT adaptation
Arianna Bisazza | Nick Ruiz | Marcello Federico
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper compares techniques to combine diverse parallel corpora for domain-specific phrase-based SMT system training. We address a common scenario where little in-domain data is available for the task, but where large background models exist for the same language pair. In particular, we focus on phrase table fill-up: a method that effectively exploits background knowledge to improve model coverage, while preserving the more reliable information coming from the in-domain corpus. We present experiments on an emerging transcribed speech translation task – the TED talks. While performing similarly in terms of BLEU and NIST scores to the popular log-linear and linear interpolation techniques, filled-up translation models are more compact and easy to tune by minimum error training.

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Left language model state for syntactic machine translation
Kenneth Heafield | Hieu Hoang | Philipp Koehn | Tetsuo Kiso | Marcello Federico
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

Many syntactic machine translation decoders, including Moses, cdec, and Joshua, implement bottom-up dynamic programming to integrate N-gram language model probabilities into hypothesis scoring. These decoders concatenate hypotheses according to grammar rules, yielding larger hypotheses and eventually complete translations. When hypotheses are concatenated, the language model score is adjusted to account for boundary-crossing n-grams. Words on the boundary of each hypothesis are encoded in state, consisting of left state (the first few words) and right state (the last few words). We speed concatenation by encoding left state using data structure pointers in lieu of vocabulary indices and by avoiding unnecessary queries. To increase the decoder’s opportunities to recombine hypothesis, we minimize the number of words encoded by left state. This has the effect of reducing search errors made by the decoder. The resulting gain in model score is smaller than for right state minimization, which we explain by observing a relationship between state minimization and language model probability. With a fixed cube pruning pop limit, we show a 3-6% reduction in CPU time and improved model scores. Reducing the pop limit to the point where model scores tie the baseline yields a net 11% reduction in CPU time.

2010

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Overview of the IWSLT 2010 evaluation campaign
Michael Paul | Marcello Federico | Sebastian Stüker
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper gives an overview of the evaluation campaign results of the 7th International Workshop on Spoken Language Translation (IWSLT 2010)1. This year, we focused on three spoken language tasks: (1) public speeches on a variety of topics (TALK) from English to French, (2) spoken dialog in travel situations (DIALOG) between Chinese and English, and (3) traveling expressions (BTEC) from Arabic, Turkish, and French to English. In total, 28 teams (including 7 firsttime participants) took part in the shared tasks, submitting 60 primary and 112 contrastive runs. Automatic and subjective evaluations of the primary runs were carried out in order to investigate the impact of different communication modalities, spoken language styles and semantic context on automatic speech recognition (ASR) and machine translation (MT) system performances.

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FBK @ IWSLT 2010
Arianna Bisazza | Ioannis Klasinas | Mauro Cettolo | Marcello Federico
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

This year FBK took part in the BTEC translation task, with source languages Arabic and Turkish and target language English, and in the new TALK task, source English and target French. We worked in the framework of phrase-based statistical machine translation aiming to improve coverage of models in presence of rich morphology, on one side, and to make better use of available resources through data selection techniques. New morphological segmentation rules were developed for Turkish-English. The combination of several Turkish segmentation schemes into a lattice input led to an improvement wrt to last year. The use of additional training data was explored for Arabic-English, while on the English to French task improvement was achieved over a strong baseline by automatically selecting relevant and high quality data from the available training corpora.

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Mining parallel fragments from comparable texts
Mauro Cettolo | Marcello Federico | Nicola Bertoldi
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

This paper proposes a novel method for exploiting comparable documents to generate parallel data for machine translation. First, each source document is paired to each sentence of the corresponding target document; second, partial phrase alignments are computed within the paired texts; finally, fragment pairs across linked phrase-pairs are extracted. The algorithm has been tested on two recent challenging news translation tasks. Results show that mining for parallel fragments is more effective than mining for parallel sentences, and that comparable in-domain texts can be more valuable than parallel out-of-domain texts.

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Modelling pronominal anaphora in statistical machine translation
Christian Hardmeier | Marcello Federico
Proceedings of the 7th International Workshop on Spoken Language Translation: Papers

Current Statistical Machine Translation (SMT) systems translate texts sentence by sentence without considering any cross-sentential context. Assuming independence between sentences makes it difficult to take certain translation decisions when the necessary information cannot be determined locally. We argue for the necessity to include crosssentence dependencies in SMT. As a case in point, we study the problem of pronominal anaphora translation by manually evaluating German-English SMT output. We then present a word dependency model for SMT, which can represent links between word pairs in the same or in different sentences. We use this model to integrate the output of a coreference resolution system into English-German SMT with a view to improving the translation of anaphoric pronouns.

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FBK at WMT 2010: Word Lattices for Morphological Reduction and Chunk-Based Reordering
Christian Hardmeier | Arianna Bisazza | Marcello Federico
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Chunk-Based Verb Reordering in VSO Sentences for Arabic-English Statistical Machine Translation
Arianna Bisazza | Marcello Federico
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Towards Cross-Lingual Textual Entailment
Yashar Mehdad | Matteo Negri | Marcello Federico
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Statistical Machine Translation of Texts with Misspelled Words
Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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FBK at IWSLT 2009
Nicola Bertoldi | Arianna Bisazza | Mauro Cettolo | Germán Sanchis-Trilles | Marcello Federico
Proceedings of the 6th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper reports on the participation of FBK at the IWSLT 2009 Evaluation. This year we worked on the Arabic-English and Turkish-English BTEC tasks with a special effort on linguistic preprocessing techniques involving morphological segmentation. In addition, we investigated the adaptation problem in the development of systems for the Chinese-English and English-Chinese challenge tasks; in particular, we explored different ways for clustering training data into topic or dialog-specific subsets: by producing (and combining) smaller but more focused models, we intended to make better use of the available training data, with the ultimate purpose of improving translation quality.

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Morphological pre-processing for Turkish to English statistical machine translation
Arianna Bisazza | Marcello Federico
Proceedings of the 6th International Workshop on Spoken Language Translation: Papers

We tried to cope with the complex morphology of Turkish by applying different schemes of morphological word segmentation to the training and test data of a phrase-based statistical machine translation system. These techniques allow for a considerable reduction of the training dictionary, and lower the out-of-vocabulary rate of the test set. By minimizing differences between lexical granularities of Turkish and English we can produce more refined alignments and a better modeling of the translation task. Morphological segmentation is highly language dependent and requires a fair amount of linguistic knowledge in its development phase. Yet it is fast and light-weight – does not involve syntax – and appears to benefit our IWSLT09 system: our best segmentation scheme associated to a simple lexical approximation technique achieved a 50% reduction of out-of-vocabulary rate and over 5 point BLEU improvement above the baseline.

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Online language model adaptation for spoken dialog translation
Germán Sanchis-Trilles | Mauro Cettolo | Nicola Bertoldi | Marcello Federico
Proceedings of the 6th International Workshop on Spoken Language Translation: Papers

This paper focuses on the problem of language model adaptation in the context of Chinese-English cross-lingual dialogs, as set-up by the challenge task of the IWSLT 2009 Evaluation Campaign. Mixtures of n-gram language models are investigated, which are obtained by clustering bilingual training data according to different available human annotations, respectively, at the dialog level, turn level, and dialog act level. For the latter case, clustering of IWSLT data was in fact induced through a comparable Italian-English parallel corpus provided with dialog act annotations. For the sake of adaptation, mixture weight estimation is performed either at the level of single source sentence or test set. Estimated weights are then transferred to the target language mixture model. Experimental results show that, by training different specific language models weighted according to the actual input instead of using a single target language model, significant gains in terms of perplexity and BLEU can be achieved.

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Domain Adaptation for Statistical Machine Translation with Monolingual Resources
Nicola Bertoldi | Marcello Federico
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

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FBK @ IWSLT-2008.
Nicola Bertoldi | Roldano Cattoni | Marcello Federico | Madalina Barbaiani
Proceedings of the 5th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper reports on the participation of FBK at the IWSLT 2008 Evaluation. Main effort has been spent on the Chinese-Spanish Pivot task. We implemented four methods to perform pivot translation. The results on the IWSLT 2008 test data show that our original method for generating training data through random sampling outperforms the best methods based on coupling translation systems. FBK also participated in the Chinese-English Challenge task and the Chinese-English and Chinese-Spanish BTEC tasks, employing the standard state-of-the-art MT system Moses Toolkit.

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Phrase-based statistical machine translation with pivot languages.
Nicola Bertoldi | Madalina Barbaiani | Marcello Federico | Roldano Cattoni
Proceedings of the 5th International Workshop on Spoken Language Translation: Papers

Translation with pivot languages has recently gained attention as a means to circumvent the data bottleneck of statistical machine translation (SMT). This paper tries to give a mathematically sound formulation of the various approaches presented in the literature and introduces new methods for training alignment models through pivot languages. We present experimental results on Chinese-Spanish translation via English, on a popular traveling domain task. In contrast to previous literature, we report experimental results by using parallel corpora that are either disjoint or overlapped on the pivot language side. Finally, our original method for generating training data through random sampling shows to perform as well as the best methods based on the coupling of translation systems.

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Shallow-Syntax Phrase-Based Translation: Joint versus Factored String-to-Chunk Models
Mauro Cettolo | Marcello Federico | Daniele Pighin | Nicola Bertoldi
Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers

This work extends phrase-based statistical MT (SMT) with shallow syntax dependencies. Two string-to-chunks translation models are proposed: a factored model, which augments phrase-based SMT with layered dependencies, and a joint model, that extends the phrase translation table with microtags, i.e. per-word projections of chunk labels. Both rely on n-gram models of target sequences with different granularity: single words, micro-tags, chunks. In particular, n-grams defined over syntactic chunks should model syntactic constraints coping with word-group movements. Experimental analysis and evaluation conducted on two popular Chinese-English tasks suggest that the shallow-syntax joint-translation model has potential to outperform state-of-the-art phrase-based translation, with a reasonable computational overhead.

2007

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Better n-best translations through generative n-gram language models
Boxing Chen | Marcello Federico | Mauro Cettolo
Proceedings of Machine Translation Summit XI: Papers

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POS-based reordering models for statistical machine translation
Deepa Gupta | Mauro Cettolo | Marcello Federico
Proceedings of Machine Translation Summit XI: Papers

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Moses: Open Source Toolkit for Statistical Machine Translation
Philipp Koehn | Hieu Hoang | Alexandra Birch | Chris Callison-Burch | Marcello Federico | Nicola Bertoldi | Brooke Cowan | Wade Shen | Christine Moran | Richard Zens | Chris Dyer | Ondřej Bojar | Alexandra Constantin | Evan Herbst
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

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Efficient Handling of N-gram Language Models for Statistical Machine Translation
Marcello Federico | Mauro Cettolo
Proceedings of the Second Workshop on Statistical Machine Translation

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FBK@IWSLT 2007
Nicola Bertoldi | Mauro Cettolo | Roldano Cattoni | Marcello Federico
Proceedings of the Fourth International Workshop on Spoken Language Translation

This paper reports on the participation of FBK (formerly ITC-irst) at the IWSLT 2007 Evaluation. FBK participated in three tasks, namely Chinese-to-English, Japanese-to-English, and Italian-to-English. With respect to last year, translation systems were developed with the Moses Toolkit and the IRSTLM library, both available as open source software. Moreover, several novel ideas were investigated: the use of confusion networks in input to manage ambiguity in punctuation, the estimation of an additional language model by means of the Google’s Web 1T 5-gram collection, the combination of true case and lower case language models, and finally the use of multiple phrase-tables. By working on top of a state-of-the art baseline, experiments showed that the above methods accounted for significant BLEU score improvements.

2006

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Exploiting Word Transformation in Statistical Machine Translation from Spanish to English
Deepa Gupta | Marcello Federico
Proceedings of the 11th Annual conference of the European Association for Machine Translation

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Maximum Entropy Tagging with Binary and Real-Valued Features
Vanessa Sandrini | Marcello Federico | Mauro Cettolo
Proceedings of the Workshop on Learning Structured Information in Natural Language Applications

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Morpho-syntactic Information for Automatic Error Analysis of Statistical Machine Translation Output
Maja Popović | Adrià de Gispert | Deepa Gupta | Patrik Lambert | Hermann Ney | José B. Mariño | Marcello Federico | Rafael Banchs
Proceedings on the Workshop on Statistical Machine Translation

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How Many Bits Are Needed To Store Probabilities for Phrase-Based Translation?
Marcello Federico | Nicola Bertoldi
Proceedings on the Workshop on Statistical Machine Translation

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The ITC-irst SMT system for IWSLT 2006
Boxing Chen | Roldano Cattoni | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

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The JHU workshop 2006 IWSLT system
Wade Shen | Richard Zens | Nicola Bertoldi | Marcello Federico
Proceedings of the Third International Workshop on Spoken Language Translation: Evaluation Campaign

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Reordering rules for phrase-based statistical machine translation
Boxing Chen | Mauro Cettolo | Marcello Federico
Proceedings of the Third International Workshop on Spoken Language Translation: Papers

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A Web-based Demonstrator of a Multi-lingual Phrase-based Translation System
Roldano Cattoni | Nicola Bertoldi | Mauro Cettolo | Boxing Chen | Marcello Federico
Demonstrations

2005

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The ITC-irst SMT System for IWSLT-2005
Boxing Chen | Roldano Cattoni | Nicola Bertoldi | Mauro Cettolo | Marcello Federico
Proceedings of the Second International Workshop on Spoken Language Translation

2004

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Overview of the IWSLT evaluation campaign
Yasuhiro Akiba | Marcello Federico | Noriko Kando | Hiromi Nakaiwa | Michael Paul | Jun’ichi Tsujii
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

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The ITC-irst statistical machine translation system for IWSLT-
Nicola Bertoldi | Roldano Cattoni | Mauro Cettolo | Marcello Federico
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

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Minimum error training of log-linear translation models
Mauro Cettolo | Marcello Federico
Proceedings of the First International Workshop on Spoken Language Translation: Papers

2002

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Bootstrapping Named Entity Recognition for Italian Broadcast News
Marcello Federico | Nicola Bertoldi | Vanessa Sandrini
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

2000

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Development and Evaluation of an Italian Broadcast News Corpus
Marcello Federico | Dimitri Giordani | Paolo Coletti
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)

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