Ondřej Bojar

Also published as: Ondrej Bojar


2021

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Sequence Length is a Domain: Length-based Overfitting in Transformer Models
Dusan Varis | Ondřej Bojar
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Transformer-based sequence-to-sequence architectures, while achieving state-of-the-art results on a large number of NLP tasks, can still suffer from overfitting during training. In practice, this is usually countered either by applying regularization methods (e.g. dropout, L2-regularization) or by providing huge amounts of training data. Additionally, Transformer and other architectures are known to struggle when generating very long sequences. For example, in machine translation, the neural-based systems perform worse on very long sequences when compared to the preceding phrase-based translation approaches (Koehn and Knowles, 2017). We present results which suggest that the issue might also be in the mismatch between the length distributions of the training and validation data combined with the aforementioned tendency of the neural networks to overfit to the training data. We demonstrate on a simple string editing tasks and a machine translation task that the Transformer model performance drops significantly when facing sequences of length diverging from the length distribution in the training data. Additionally, we show that the observed drop in performance is due to the hypothesis length corresponding to the lengths seen by the model during training rather than the length of the input sequence.

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Neural Machine Translation Quality and Post-Editing Performance
Vilém Zouhar | Martin Popel | Ondřej Bojar | Aleš Tamchyna
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

We test the natural expectation that using MT in professional translation saves human processing time. The last such study was carried out by Sanchez-Torron and Koehn (2016) with phrase-based MT, artificially reducing the translation quality. In contrast, we focus on neural MT (NMT) of high quality, which has become the state-of-the-art approach since then and also got adopted by most translation companies. Through an experimental study involving over 30 professional translators for English -> Czech translation, we examine the relationship between NMT performance and post-editing time and quality. Across all models, we found that better MT systems indeed lead to fewer changes in the sentences in this industry setting. The relation between system quality and post-editing time is however not straightforward and, contrary to the results on phrase-based MT, BLEU is definitely not a stable predictor of the time or final output quality.

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Operating a Complex SLT System with Speakers and Human Interpreters
Ondřej Bojar | Vojtěch Srdečný | Rishu Kumar | Otakar Smrž | Felix Schneider | Barry Haddow | Phil Williams | Chiara Canton
Proceedings of the 1st Workshop on Automatic Spoken Language Translation in Real-World Settings (ASLTRW)

We describe our experience with providing automatic simultaneous spoken language translation for an event with human interpreters. We provide a detailed overview of the systems we use, focusing on their interconnection and the issues it brings. We present our tools to monitor the pipeline and a web application to present the results of our SLT pipeline to the end users. Finally, we discuss various challenges we encountered, their possible solutions and we suggest improvements for future deployments.

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Backtranslation Feedback Improves User Confidence in MT, Not Quality
Vilém Zouhar | Michal Novák | Matúš Žilinec | Ondřej Bojar | Mateo Obregón | Robin L. Hill | Frédéric Blain | Marina Fomicheva | Lucia Specia | Lisa Yankovskaya
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Translating text into a language unknown to the text’s author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by showing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. In this paper, we describe an experiment on outbound translation from English to Czech and Estonian. We examine the effects of each proposed feedback module and further focus on how the quality of machine translation systems influence these findings and the user perception of success. We show that backward translation feedback has a mixed effect on the whole process: it increases user confidence in the produced translation, but not the objective quality.

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Explainable Quality Estimation: CUNI Eval4NLP Submission
Peter Polák | Muskaan Singh | Ondřej Bojar
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

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SLTEV: Comprehensive Evaluation of Spoken Language Translation
Ebrahim Ansari | Ondřej Bojar | Barry Haddow | Mohammad Mahmoudi
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

Automatic evaluation of Machine Translation (MT) quality has been investigated over several decades. Spoken Language Translation (SLT), esp. when simultaneous, needs to consider additional criteria and does not have a standard evaluation procedure and a widely used toolkit. To fill the gap, we develop SLTev, an open-source tool for assessing SLT in a comprehensive way. SLTev reports the quality, latency, and stability of an SLT candidate output based on the time-stamped transcript and reference translation into a target language. For quality, we rely on sacreBLEU which provides MT evaluation measures such as chrF or BLEU. For latency, we propose two new scoring techniques. For stability, we extend the previously defined measures with a normalized Flicker in our work. We also propose a new averaging of older measures. A preliminary version of SLTev was used in the IWSLT 2020 shared task. Moreover, a growing collection of test datasets directly accessible by SLTev are provided for system evaluation comparable across papers.

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

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

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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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Detecting Post-Edited References and Their Effect on Human Evaluation
Věra Kloudová | Ondřej Bojar | Martin Popel
Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)

This paper provides a quick overview of possible methods how to detect that reference translations were actually created by post-editing an MT system. Two methods based on automatic metrics are presented: BLEU difference between the suspected MT and some other good MT and BLEU difference using additional references. These two methods revealed a suspicion that the WMT 2020 Czech reference is based on MT. The suspicion was confirmed in a manual analysis by finding concrete proofs of the post-editing procedure in particular sentences. Finally, a typology of post-editing changes is presented where typical errors or changes made by the post-editor or errors adopted from the MT are classified.

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Proceedings of the 8th Workshop on Asian Translation (WAT2021)
Toshiaki Nakazawa | Hideki Nakayama | Isao Goto | Hideya Mino | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Shohei Higashiyama | Hiroshi Manabe | Win Pa Pa | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Katsuhito Sudoh | Sadao Kurohashi | Pushpak Bhattacharyya
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

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Overview of the 8th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Chenhui Chu | Akiko Eriguchi | Kaori Abe | Yusuke Oda | Sadao Kurohashi
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021). For the WAT2021, 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation. We also accepted 5 research papers. About 2,100 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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NLPHut’s Participation at WAT2021
Shantipriya Parida | Subhadarshi Panda | Ketan Kotwal | Amulya Ratna Dash | Satya Ranjan Dash | Yashvardhan Sharma | Petr Motlicek | Ondřej Bojar
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

This paper provides the description of shared tasks to the WAT 2021 by our team “NLPHut”. We have participated in the English→Hindi Multimodal translation task, English→Malayalam Multimodal translation task, and Indic Multi-lingual translation task. We have used the state-of-the-art Transformer model with language tags in different settings for the translation task and proposed a novel “region-specific” caption generation approach using a combination of image CNN and LSTM for the Hindi and Malayalam image captioning. Our submission tops in English→Malayalam Multimodal translation task (text-only translation, and Malayalam caption), and ranks second-best in English→Hindi Multimodal translation task (text-only translation, and Hindi caption). Our submissions have also performed well in the Indic Multilingual translation tasks.

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End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages
Josef Jon | João Paulo Aires | Dusan Varis | Ondřej Bojar
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Lexically constrained machine translation allows the user to manipulate the output sentence by enforcing the presence or absence of certain words and phrases. Although current approaches can enforce terms to appear in the translation, they often struggle to make the constraint word form agree with the rest of the generated output. Our manual analysis shows that 46% of the errors in the output of a baseline constrained model for English to Czech translation are related to agreement. We investigate mechanisms to allow neural machine translation to infer the correct word inflection given lemmatized constraints. In particular, we focus on methods based on training the model with constraints provided as part of the input sequence. Our experiments on English-Czech language pair show that this approach improves translation of constrained terms in both automatic and manual evaluation by reducing errors in agreement. Our approach thus eliminates inflection errors, without introducing new errors or decreasing overall quality of the translation.

2020

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Proceedings of the 7th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Win Pa Pa | Ondřej Bojar | Shantipriya Parida | Isao Goto | Hidaya Mino | Hiroshi Manabe | Katsuhito Sudoh | Sadao Kurohashi | Pushpak Bhattacharyya
Proceedings of the 7th Workshop on Asian Translation

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Overview of the 7th Workshop on Asian Translation
Toshiaki Nakazawa | Hideki Nakayama | Chenchen Ding | Raj Dabre | Shohei Higashiyama | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Shantipriya Parida | Ondřej Bojar | Sadao Kurohashi
Proceedings of the 7th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020). For the WAT2020, 20 teams participated in the shared tasks and 14 teams submitted their translation results for the human evaluation. We also received 12 research paper submissions out of which 7 were accepted. About 500 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.

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ODIANLP’s Participation in WAT2020
Shantipriya Parida | Petr Motlicek | Amulya Ratna Dash | Satya Ranjan Dash | Debasish Kumar Mallick | Satya Prakash Biswal | Priyanka Pattnaik | Biranchi Narayan Nayak | Ondřej Bojar
Proceedings of the 7th Workshop on Asian Translation

This paper describes the ODIANLP submission to WAT 2020. We have participated in the English-Hindi Multimodal task and Indic task. We have used the state-of-the-art Transformer model for the translation task and InceptionResNetV2 for the Hindi Image Captioning task. Our submission tops in English->Hindi Multimodal task in its track and Odia<->English translation tasks. Also, our submissions performed well in the Indic Multilingual tasks.

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Unsupervised Multilingual Sentence Embeddings for Parallel Corpus Mining
Ivana Kvapilíková | Mikel Artetxe | Gorka Labaka | Eneko Agirre | Ondřej Bojar
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Existing models of multilingual sentence embeddings require large parallel data resources which are not available for low-resource languages. We propose a novel unsupervised method to derive multilingual sentence embeddings relying only on monolingual data. We first produce a synthetic parallel corpus using unsupervised machine translation, and use it to fine-tune a pretrained cross-lingual masked language model (XLM) to derive the multilingual sentence representations. The quality of the representations is evaluated on two parallel corpus mining tasks with improvements of up to 22 F1 points over vanilla XLM. In addition, we observe that a single synthetic bilingual corpus is able to improve results for other language pairs.

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Proceedings of the Fifth Conference on Machine Translation
Loïc Barrault | Ondřej Bojar | Fethi Bougares | Rajen Chatterjee | Marta R. Costa-jussà | Christian Federmann | Mark Fishel | Alexander Fraser | Yvette Graham | Paco Guzman | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Makoto Morishita | Christof Monz | Masaaki Nagata | Toshiaki Nakazawa | Matteo Negri
Proceedings of the Fifth Conference on Machine Translation

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Findings of the 2020 Conference on Machine Translation (WMT20)
Loïc Barrault | Magdalena Biesialska | Ondřej Bojar | Marta R. Costa-jussà | Christian Federmann | Yvette Graham | Roman Grundkiewicz | Barry Haddow | Matthias Huck | Eric Joanis | Tom Kocmi | Philipp Koehn | Chi-kiu Lo | Nikola Ljubešić | Christof Monz | Makoto Morishita | Masaaki Nagata | Toshiaki Nakazawa | Santanu Pal | Matt Post | Marcos Zampieri
Proceedings of the Fifth Conference on Machine Translation

This paper presents the results of the news translation task and the similar language translation task, both organised alongside the Conference on Machine Translation (WMT) 2020. In the news task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting mainly of news stories. The task was also opened up to additional test suites to probe specific aspects of translation. In the similar language translation task, participants built machine translation systems for translating between closely related pairs of languages.

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WMT20 Document-Level Markable Error Exploration
Vilém Zouhar | Tereza Vojtěchová | Ondřej Bojar
Proceedings of the Fifth Conference on Machine Translation

Even though sentence-centric metrics are used widely in machine translation evaluation, document-level performance is at least equally important for professional usage. In this paper, we bring attention to detailed document-level evaluation focused on markables (expressions bearing most of the document meaning) and the negative impact of various markable error phenomena on the translation. For an annotation experiment of two phases, we chose Czech and English documents translated by systems submitted to WMT20 News Translation Task. These documents are from the News, Audit and Lease domains. We show that the quality and also the kind of errors varies significantly among the domains. This systematic variance is in contrast to the automatic evaluation results. We inspect which specific markables are problematic for MT systems and conclude with an analysis of the effect of markable error types on the MT performance measured by humans and automatic evaluation tools.

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Results of the WMT20 Metrics Shared Task
Nitika Mathur | Johnny Wei | Markus Freitag | Qingsong Ma | Ondřej Bojar
Proceedings of the Fifth Conference on Machine Translation

This paper presents the results of the WMT20 Metrics Shared Task. Participants were asked to score the outputs of the translation systems competing in the WMT20 News Translation Task with automatic metrics. Ten research groups submitted 27 metrics, four of which are reference-less “metrics”. In addition, we computed five baseline metrics, including sentBLEU, BLEU, TER and using the SacreBLEU scorer. All metrics were evaluated on how well they correlate at the system-, document- and segment-level with the WMT20 official human scores. We present an extensive analysis on influence of different reference translations on metric reliability, how well automatic metrics score human translations, and we also flag major discrepancies between metric and human scores when evaluating MT systems. Finally, we investigate whether we can use automatic metrics to flag incorrect human ratings.

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CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20
Ivana Kvapilíková | Tom Kocmi | Ondřej Bojar
Proceedings of the Fifth Conference on Machine Translation

This paper presents a description of CUNI systems submitted to the WMT20 task on unsupervised and very low-resource supervised machine translation between German and Upper Sorbian. We experimented with training on synthetic data and pre-training on a related language pair. In the fully unsupervised scenario, we achieved 25.5 and 23.7 BLEU translating from and into Upper Sorbian, respectively. Our low-resource systems relied on transfer learning from German-Czech parallel data and achieved 57.4 BLEU and 56.1 BLEU, which is an improvement of 10 BLEU points over the baseline trained only on the available small German-Upper Sorbian parallel corpus.

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OdiEnCorp 2.0: Odia-English Parallel Corpus for Machine Translation
Shantipriya Parida | Satya Ranjan Dash | Ondřej Bojar | Petr Motlicek | Priyanka Pattnaik | Debasish Kumar Mallick
Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation

The preparation of parallel corpora is a challenging task, particularly for languages that suffer from under-representation in the digital world. In a multi-lingual country like India, the need for such parallel corpora is stringent for several low-resource languages. In this work, we provide an extended English-Odia parallel corpus, OdiEnCorp 2.0, aiming particularly at Neural Machine Translation (NMT) systems which will help translate English↔Odia. OdiEnCorp 2.0 includes existing English-Odia corpora and we extended the collection by several other methods of data acquisition: parallel data scraping from many websites, including Odia Wikipedia, but also optical character recognition (OCR) to extract parallel data from scanned images. Our OCR-based data extraction approach for building a parallel corpus is suitable for other low resource languages that lack in online content. The resulting OdiEnCorp 2.0 contains 98,302 sentences and 1.69 million English and 1.47 million Odia tokens. To the best of our knowledge, OdiEnCorp 2.0 is the largest Odia-English parallel corpus covering different domains and available freely for non-commercial and research purposes.

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COSTRA 1.0: A Dataset of Complex Sentence Transformations
Petra Barancikova | Ondřej Bojar
Proceedings of the 12th Language Resources and Evaluation Conference

We present COSTRA 1.0, a dataset of complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. This first version of the dataset is limited to sentences in Czech but the construction method is universal and we plan to use it also for other languages. The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation. The hope is that with this dataset, we should be able to test semantic properties of sentence embeddings and perhaps even to find some topologically interesting “skeleton” in the sentence embedding space. A preliminary analysis using LASER, multi-purpose multi-lingual sentence embeddings suggests that the LASER space does not exhibit the desired properties.

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Large Corpus of Czech Parliament Plenary Hearings
Jonas Kratochvil | Peter Polak | Ondrej Bojar
Proceedings of the 12th Language Resources and Evaluation Conference

We present a large corpus of Czech parliament plenary sessions. The corpus consists of approximately 1200 hours of speech data and corresponding text transcriptions. The whole corpus has been segmented to short audio segments making it suitable for both training and evaluation of automatic speech recognition (ASR) systems. The source language of the corpus is Czech, which makes it a valuable resource for future research as only a few public datasets are available in the Czech language. We complement the data release with experiments of two baseline ASR systems trained on the presented data: the more traditional approach implemented in the Kaldi ASRtoolkit which combines hidden Markov models and deep neural networks (NN) and a modern ASR architecture implemented in Jaspertoolkit which uses deep NNs in an end-to-end fashion.

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Two Huge Title and Keyword Generation Corpora of Research Articles
Erion Çano | Ondřej Bojar
Proceedings of the 12th Language Resources and Evaluation Conference

Recent developments in sequence-to-sequence learning with neural networks have considerably improved the quality of automatically generated text summaries and document keywords, stipulating the need for even bigger training corpora. Metadata of research articles are usually easy to find online and can be used to perform research on various tasks. In this paper, we introduce two huge datasets for text summarization (OAGSX) and keyword generation (OAGKX) research, containing 34 million and 23 million records, respectively. The data were retrieved from the Open Academic Graph which is a network of research profiles and publications. We carefully processed each record and also tried several extractive and abstractive methods of both tasks to create performance baselines for other researchers. We further illustrate the performance of those methods previewing their outputs. In the near future, we would like to apply topic modeling on the two sets to derive subsets of research articles from more specific disciplines.

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Outbound Translation User Interface Ptakopět: A Pilot Study
Vilém Zouhar | Ondřej Bojar
Proceedings of the 12th Language Resources and Evaluation Conference

It is not uncommon for Internet users to have to produce a text in a foreign language they have very little knowledge of and are unable to verify the translation quality. We call the task “outbound translation” and explore it by introducing an open-source modular system Ptakopět. Its main purpose is to inspect human interaction with MT systems enhanced with additional subsystems, such as backward translation and quality estimation. We follow up with an experiment on (Czech) human annotators tasked to produce questions in a language they do not speak (German), with the help of Ptakopět. We focus on three real-world use cases (communication with IT support, describing administrative issues and asking encyclopedic questions) from which we gain insight into different strategies users take when faced with outbound translation tasks. Round trip translation is known to be unreliable for evaluating MT systems but our experimental evaluation documents that it works very well for users, at least on MT systems of mid-range quality.

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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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CUNI Neural ASR with Phoneme-Level Intermediate Step for~Non-Native~SLT at IWSLT 2020
Peter Polák | Sangeet Sagar | Dominik Macháček | Ondřej Bojar
Proceedings of the 17th International Conference on Spoken Language Translation

In this paper, we present our submission to the Non-Native Speech Translation Task for IWSLT 2020. Our main contribution is a proposed speech recognition pipeline that consists of an acoustic model and a phoneme-to-grapheme model. As an intermediate representation, we utilize phonemes. We demonstrate that the proposed pipeline surpasses commercially used automatic speech recognition (ASR) and submit it into the ASR track. We complement this ASR with off-the-shelf MT systems to take part also in the speech translation track.

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ELITR Non-Native Speech Translation at IWSLT 2020
Dominik Macháček | Jonáš Kratochvíl | Sangeet Sagar | Matúš Žilinec | Ondřej Bojar | Thai-Son Nguyen | Felix Schneider | Philip Williams | Yuekun Yao
Proceedings of the 17th International Conference on Spoken Language Translation

This paper is an ELITR system submission for the non-native speech translation task at IWSLT 2020. We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT. We select our primary candidates from a pool of pre-existing systems, develop a new end-to-end general ASR system, and a hybrid ASR trained on non-native speech. The provided small validation set prevents us from carrying out a complex validation, but we submit all the unselected candidates for contrastive evaluation on the test set.

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

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

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Efficiently Reusing Old Models Across Languages via Transfer Learning
Tom Kocmi | Ondřej Bojar
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

Recent progress in neural machine translation (NMT) is directed towards larger neural networks trained on an increasing amount of hardware resources. As a result, NMT models are costly to train, both financially, due to the electricity and hardware cost, and environmentally, due to the carbon footprint. It is especially true in transfer learning for its additional cost of training the “parent” model before transferring knowledge and training the desired “child” model. In this paper, we propose a simple method of re-using an already trained model for different language pairs where there is no need for modifications in model architecture. Our approach does not need a separate parent model for each investigated language pair, as it is typical in NMT transfer learning. To show the applicability of our method, we recycle a Transformer model trained by different researchers and use it to seed models for different language pairs. We achieve better translation quality and shorter convergence times than when training from random initialization.

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

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

2019

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Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation
Dušan Variš | Ondřej Bojar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

This work presents our ongoing research of unsupervised pretraining in neural machine translation (NMT). In our method, we initialize the weights of the encoder and decoder with two language models that are trained with monolingual data and then fine-tune the model on parallel data using Elastic Weight Consolidation (EWC) to avoid forgetting of the original language modeling task. We compare the regularization by EWC with the previous work that focuses on regularization by language modeling objectives. The positive result is that using EWC with the decoder achieves BLEU scores similar to the previous work. However, the model converges 2-3 times faster and does not require the original unlabeled training data during the fine-tuning stage. In contrast, the regularization using EWC is less effective if the original and new tasks are not closely related. We show that initializing the bidirectional NMT encoder with a left-to-right language model and forcing the model to remember the original left-to-right language modeling task limits the learning capacity of the encoder for the whole bidirectional context.

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Proceedings of the 6th Workshop on Asian Translation
Toshiaki Nakazawa | Chenchen Ding | Raj Dabre | Anoop Kunchukuttan | Nobushige Doi | Yusuke Oda | Ondřej Bojar | Shantipriya Parida | Isao Goto | Hidaya Mino
Proceedings of the 6th Workshop on Asian Translation

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Overview of the 6th Workshop on Asian Translation
Toshiaki Nakazawa | Nobushige Doi | Shohei Higashiyama | Chenchen Ding | Raj Dabre | Hideya Mino | Isao Goto | Win Pa Pa | Anoop Kunchukuttan | Yusuke Oda | Shantipriya Parida | Ondřej Bojar | Sadao Kurohashi
Proceedings of the 6th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 6th workshop on Asian translation (WAT2019) including Ja↔En, Ja↔Zh scientific paper translation subtasks, Ja↔En, Ja↔Ko, Ja↔En patent translation subtasks, Hi↔En, My↔En, Km↔En, Ta↔En mixed domain subtasks and Ru↔Ja news commentary translation task. For the WAT2019, 25 teams participated in the shared tasks. We also received 10 research paper submissions out of which 61 were accepted. About 400 translation results were submitted to the automatic evaluation server, and selected submis- sions were manually evaluated.

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Idiap NMT System for WAT 2019 Multimodal Translation Task
Shantipriya Parida | Ondřej Bojar | Petr Motlicek
Proceedings of the 6th Workshop on Asian Translation

This paper describes the Idiap submission to WAT 2019 for the English-Hindi Multi-Modal Translation Task. We have used the state-of-the-art Transformer model and utilized the IITB English-Hindi parallel corpus as an additional data source. Among the different tracks of the multi-modal task, we have participated in the “Text-Only” track for the evaluation and challenge test sets. Our submission tops in its track among the competitors in terms of both automatic and manual evaluation. Based on automatic scores, our text-only submission also outperforms systems that consider visual information in the “multi-modal translation” task.

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Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

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Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

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Findings of the 2019 Conference on Machine Translation (WMT19)
Loïc Barrault | Ondřej Bojar | Marta R. Costa-jussà | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Philipp Koehn | Shervin Malmasi | Christof Monz | Mathias Müller | Santanu Pal | Matt Post | Marcos Zampieri
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019. Participants were asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. The task was also opened up to additional test suites to probe specific aspects of translation.

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Results of the WMT19 Metrics Shared Task: Segment-Level and Strong MT Systems Pose Big Challenges
Qingsong Ma | Johnny Wei | Ondřej Bojar | Yvette Graham
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper presents the results of the WMT19 Metrics Shared Task. Participants were asked to score the outputs of the translations systems competing in the WMT19 News Translation Task with automatic metrics. 13 research groups submitted 24 metrics, 10 of which are reference-less “metrics” and constitute submissions to the joint task with WMT19 Quality Estimation Task, “QE as a Metric”. In addition, we computed 11 baseline metrics, with 8 commonly applied baselines (BLEU, SentBLEU, NIST, WER, PER, TER, CDER, and chrF) and 3 reimplementations (chrF+, sacreBLEU-BLEU, and sacreBLEU-chrF). Metrics were evaluated on the system level, how well a given metric correlates with the WMT19 official manual ranking, and segment level, how well the metric correlates with human judgements of segment quality. This year, we use direct assessment (DA) as our only form of manual evaluation.

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CUNI Submission for Low-Resource Languages in WMT News 2019
Tom Kocmi | Ondřej Bojar
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes the CUNI submission to the WMT 2019 News Translation Shared Task for the low-resource languages: Gujarati-English and Kazakh-English. We participated in both language pairs in both translation directions. Our system combines transfer learning from a different high-resource language pair followed by training on backtranslated monolingual data. Thanks to the simultaneous training in both directions, we can iterate the backtranslation process. We are using the Transformer model in a constrained submission.

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CUNI Systems for the Unsupervised News Translation Task in WMT 2019
Ivana Kvapilíková | Dominik Macháček | Ondřej Bojar
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

In this paper we describe the CUNI translation system used for the unsupervised news shared task of the ACL 2019 Fourth Conference on Machine Translation (WMT19). We follow the strategy of Artetxe ae at. (2018b), creating a seed phrase-based system where the phrase table is initialized from cross-lingual embedding mappings trained on monolingual data, followed by a neural machine translation system trained on synthetic parallel data. The synthetic corpus was produced from a monolingual corpus by a tuned PBMT model refined through iterative back-translation. We further focus on the handling of named entities, i.e. the part of vocabulary where the cross-lingual embedding mapping suffers most. Our system reaches a BLEU score of 15.3 on the German-Czech WMT19 shared task.

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English-Czech Systems in WMT19: Document-Level Transformer
Martin Popel | Dominik Macháček | Michal Auersperger | Ondřej Bojar | Pavel Pecina
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We describe our NMT systems submitted to the WMT19 shared task in English→Czech news translation. Our systems are based on the Transformer model implemented in either Tensor2Tensor (T2T) or Marian framework. We aimed at improving the adequacy and coherence of translated documents by enlarging the context of the source and target. Instead of translating each sentence independently, we split the document into possibly overlapping multi-sentence segments. In case of the T2T implementation, this “document-level”-trained system achieves a +0.6 BLEU improvement (p < 0.05) relative to the same system applied on isolated sentences. To assess the potential effect document-level models might have on lexical coherence, we performed a semi-automatic analysis, which revealed only a few sentences improved in this aspect. Thus, we cannot draw any conclusions from this week evidence.

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A Test Suite and Manual Evaluation of Document-Level NMT at WMT19
Kateřina Rysová | Magdaléna Rysová | Tomáš Musil | Lucie Poláková | Ondřej Bojar
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

As the quality of machine translation rises and neural machine translation (NMT) is moving from sentence to document level translations, it is becoming increasingly difficult to evaluate the output of translation systems. We provide a test suite for WMT19 aimed at assessing discourse phenomena of MT systems participating in the News Translation Task. We have manually checked the outputs and identified types of translation errors that are relevant to document-level translation.

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SAO WMT19 Test Suite: Machine Translation of Audit Reports
Tereza Vojtěchová | Michal Novák | Miloš Klouček | Ondřej Bojar
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

This paper describes a machine translation test set of documents from the auditing domain and its use as one of the “test suites” in the WMT19 News Translation Task for translation directions involving Czech, English and German. Our evaluation suggests that current MT systems optimized for the general news domain can perform quite well even in the particular domain of audit reports. The detailed manual evaluation however indicates that deep factual knowledge of the domain is necessary. For the naked eye of a non-expert, translations by many systems seem almost perfect and automatic MT evaluation with one reference is practically useless for considering these details. Furthermore, we show on a sample document from the domain of agreements that even the best systems completely fail in preserving the semantics of the agreement, namely the identity of the parties.

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Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | André Martins | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Marco Turchi | Karin Verspoor
Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)

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Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study
Erion Çano | Ondřej Bojar
Proceedings of the 12th International Conference on Natural Language Generation

Using data-driven models for solving text summarization or similar tasks has become very common in the last years. Yet most of the studies report basic accuracy scores only, and nothing is known about the ability of the proposed models to improve when trained on more data. In this paper, we define and propose three data efficiency metrics: data score efficiency, data time deficiency and overall data efficiency. We also propose a simple scheme that uses those metrics and apply it for a more comprehensive evaluation of popular methods on text summarization and title generation tasks. For the latter task, we process and release a huge collection of 35 million abstract-title pairs from scientific articles. Our results reveal that among the tested models, the Transformer is the most efficient on both tasks.

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Keyphrase Generation: A Text Summarization Struggle
Erion Çano | Ondřej Bojar
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Authors’ keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the simpler unsupervised methods, or the existing supervised ones.

2018

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Neural Monkey: The Current State and Beyond
Jindřich Helcl | Jindřich Libovický | Tom Kocmi | Tomáš Musil | Ondřej Cífka | Dušan Variš | Ondřej Bojar
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)

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Proceedings of the Third Conference on Machine Translation: Research Papers
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Lucia Specia | Marco Turchi | Karin Verspoor
Proceedings of the Third Conference on Machine Translation: Research Papers

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Trivial Transfer Learning for Low-Resource Neural Machine Translation
Tom Kocmi | Ondřej Bojar
Proceedings of the Third Conference on Machine Translation: Research Papers

Transfer learning has been proven as an effective technique for neural machine translation under low-resource conditions. Existing methods require a common target language, language relatedness, or specific training tricks and regimes. We present a simple transfer learning method, where we first train a “parent” model for a high-resource language pair and then continue the training on a low-resource pair only by replacing the training corpus. This “child” model performs significantly better than the baseline trained for low-resource pair only. We are the first to show this for targeting different languages, and we observe the improvements even for unrelated languages with different alphabets.

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Proceedings of the Third Conference on Machine Translation: Shared Task Papers
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Matt Post | Lucia Specia | Marco Turchi | Karin Verspoor
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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Findings of the 2018 Conference on Machine Translation (WMT18)
Ondřej Bojar | Christian Federmann | Mark Fishel | Yvette Graham | Barry Haddow | Philipp Koehn | Christof Monz
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018. Participants were asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories. The main metric for this task is human judgment of translation quality. This year, we also opened up the task to additional test sets to probe specific aspects of translation.

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CUNI Submissions in WMT18
Tom Kocmi | Roman Sudarikov | Ondřej Bojar
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We participated in the WMT 2018 shared news translation task in three language pairs: English-Estonian, English-Finnish, and English-Czech. Our main focus was the low-resource language pair of Estonian and English for which we utilized Finnish parallel data in a simple method. We first train a “parent model” for the high-resource language pair followed by adaptation on the related low-resource language pair. This approach brings a substantial performance boost over the baseline system trained only on Estonian-English parallel data. Our systems are based on the Transformer architecture. For the English to Czech translation, we have evaluated our last year models of hybrid phrase-based approach and neural machine translation mainly for comparison purposes.

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EvalD Reference-Less Discourse Evaluation for WMT18
Ondřej Bojar | Jiří Mírovský | Kateřina Rysová | Magdaléna Rysová
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We present the results of automatic evaluation of discourse in machine translation (MT) outputs using the EVALD tool. EVALD was originally designed and trained to assess the quality of human writing, for native speakers and foreign-language learners. MT has seen a tremendous leap in translation quality at the level of sentences and it is thus interesting to see if the human-level evaluation is becoming relevant.

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The WMT’18 Morpheval test suites for English-Czech, English-German, English-Finnish and Turkish-English
Franck Burlot | Yves Scherrer | Vinit Ravishankar | Ondřej Bojar | Stig-Arne Grönroos | Maarit Koponen | Tommi Nieminen | François Yvon
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

Progress in the quality of machine translation output calls for new automatic evaluation procedures and metrics. In this paper, we extend the Morpheval protocol introduced by Burlot and Yvon (2017) for the English-to-Czech and English-to-Latvian translation directions to three additional language pairs, and report its use to analyze the results of WMT 2018’s participants for these language pairs. Considering additional, typologically varied source and target languages also enables us to draw some generalizations regarding this morphology-oriented evaluation procedure.

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Testsuite on Czech–English Grammatical Contrasts
Silvie Cinková | Ondřej Bojar
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We present a pilot study of machine translation of selected grammatical contrasts between Czech and English in WMT18 News Translation Task. For each phenomenon, we run a dedicated test which checks if the candidate translation expresses the phenomenon as expected or not. The proposed type of analysis is not an evaluation in the strict sense because the phenomenon can be correctly translated in various ways and we anticipate only one. What is nevertheless interesting are the differences between various MT systems and the single reference translation in their general tendency in handling the given phenomenon.

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Results of the WMT18 Metrics Shared Task: Both characters and embeddings achieve good performance
Qingsong Ma | Ondřej Bojar | Yvette Graham
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper presents the results of the WMT18 Metrics Shared Task. We asked participants of this task to score the outputs of the MT systems involved in the WMT18 News Translation Task with automatic metrics. We collected scores of 10 metrics and 8 research groups. In addition to that, we computed scores of 8 standard metrics (BLEU, SentBLEU, chrF, NIST, WER, PER, TER and CDER) as baselines. The collected scores were evaluated in terms of system-level correlation (how well each metric’s scores correlate with WMT18 official manual ranking of systems) and in terms of segment-level correlation (how often a metric agrees with humans in judging the quality of a particular sentence relative to alternate outputs). This year, we employ a single kind of manual evaluation: direct assessment (DA).

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Are BLEU and Meaning Representation in Opposition?
Ondřej Cífka | Ondřej Bojar
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems. The recent attention mechanism however removes the single point in the neural network from which the source sentence representation can be extracted. We propose several variations of the attentive NMT architecture bringing this meeting point back. Empirical evaluation suggests that the better the translation quality, the worse the learned sentence representations serve in a wide range of classification and similarity tasks.

2017

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Curriculum Learning and Minibatch Bucketing in Neural Machine Translation
Tom Kocmi | Ondřej Bojar
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

We examine the effects of particular orderings of sentence pairs on the on-line training of neural machine translation (NMT). We focus on two types of such orderings: (1) ensuring that each minibatch contains sentences similar in some aspect and (2) gradual inclusion of some sentence types as the training progresses (so called “curriculum learning”). In our English-to-Czech experiments, the internal homogeneity of minibatches has no effect on the training but some of our “curricula” achieve a small improvement over the baseline.

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LanideNN: Multilingual Language Identification on Character Window
Tom Kocmi | Ondřej Bojar
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In language identification, a common first step in natural language processing, we want to automatically determine the language of some input text. Monolingual language identification assumes that the given document is written in one language. In multilingual language identification, the document is usually in two or three languages and we just want their names. We aim one step further and propose a method for textual language identification where languages can change arbitrarily and the goal is to identify the spans of each of the languages. Our method is based on Bidirectional Recurrent Neural Networks and it performs well in monolingual and multilingual language identification tasks on six datasets covering 131 languages. The method keeps the accuracy also for short documents and across domains, so it is ideal for off-the-shelf use without preparation of training data.

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Producing Unseen Morphological Variants in Statistical Machine Translation
Matthias Huck | Aleš Tamchyna | Ondřej Bojar | Alexander Fraser
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Translating into morphologically rich languages is difficult. Although the coverage of lemmas may be reasonable, many morphological variants cannot be learned from the training data. We present a statistical translation system that is able to produce these inflected word forms. Different from most previous work, we do not separate morphological prediction from lexical choice into two consecutive steps. Our approach is novel in that it is integrated in decoding and takes advantage of context information from both the source language and the target language sides.

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Proceedings of the Second Conference on Machine Translation
Ondřej Bojar | Christian Buck | Rajen Chatterjee | Christian Federmann | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Julia Kreutzer
Proceedings of the Second Conference on Machine Translation

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Findings of the 2017 Conference on Machine Translation (WMT17)
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Yvette Graham | Barry Haddow | Shujian Huang | Matthias Huck | Philipp Koehn | Qun Liu | Varvara Logacheva | Christof Monz | Matteo Negri | Matt Post | Raphael Rubino | Lucia Specia | Marco Turchi
Proceedings of the Second Conference on Machine Translation

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Findings of the WMT 2017 Biomedical Translation Shared Task
Antonio Jimeno Yepes | Aurélie Névéol | Mariana Neves | Karin Verspoor | Ondřej Bojar | Arthur Boyer | Cristian Grozea | Barry Haddow | Madeleine Kittner | Yvonne Lichtblau | Pavel Pecina | Roland Roller | Rudolf Rosa | Amy Siu | Philippe Thomas | Saskia Trescher
Proceedings of the Second Conference on Machine Translation

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CUNI submission in WMT17: Chimera goes neural
Roman Sudarikov | David Mareček | Tom Kocmi | Dušan Variš | Ondřej Bojar
Proceedings of the Second Conference on Machine Translation

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

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Results of the WMT17 Metrics Shared Task
Ondřej Bojar | Yvette Graham | Amir Kamran
Proceedings of the Second Conference on Machine Translation

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Results of the WMT17 Neural MT Training Task
Ondřej Bojar | Jindřich Helcl | Tom Kocmi | Jindřich Libovický | Tomáš Musil
Proceedings of the Second Conference on Machine Translation

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CUNI Experiments for WMT17 Metrics Task
David Mareček | Ondřej Bojar | Ondřej Hübsch | Rudolf Rosa | Dušan Variš
Proceedings of the Second Conference on Machine Translation

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CUNI System for WMT17 Automatic Post-Editing Task
Dušan Variš | Ondřej Bojar
Proceedings of the Second Conference on Machine Translation

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Variable Mini-Batch Sizing and Pre-Trained Embeddings
Mostafa Abdou | Vladan Glončák | Ondřej Bojar
Proceedings of the Second Conference on Machine Translation

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CUNI NMT System for WAT 2017 Translation Tasks
Tom Kocmi | Dušan Variš | Ondřej Bojar
Proceedings of the 4th Workshop on Asian Translation (WAT2017)

The paper presents this year’s CUNI submissions to the WAT 2017 Translation Task focusing on the Japanese-English translation, namely Scientific papers subtask, Patents subtask and Newswire subtask. We compare two neural network architectures, the standard sequence-to-sequence with attention (Seq2Seq) and an architecture using convolutional sentence encoder (FBConv2Seq), both implemented in the NMT framework Neural Monkey that we currently participate in developing. We also compare various types of preprocessing of the source Japanese sentences and their impact on the overall results. Furthermore, we include the results of our experiments with out-of-domain data obtained by combining the corpora provided for each subtask.

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An Exploration of Word Embedding Initialization in Deep-Learning Tasks
Tom Kocmi | Ondřej Bojar
Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017)

2016

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Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers
Ondřej Bojar | Christian Buck | Rajen Chatterjee | Christian Federmann | Liane Guillou | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Aurélie Névéol | Mariana Neves | Pavel Pecina | Martin Popel | Philipp Koehn | Christof Monz | Matteo Negri | Matt Post | Lucia Specia | Karin Verspoor | Jörg Tiedemann | Marco Turchi
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers
Ondřej Bojar | Christian Buck | Rajen Chatterjee | Christian Federmann | Liane Guillou | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Aurélie Névéol | Mariana Neves | Pavel Pecina | Martin Popel | Philipp Koehn | Christof Monz | Matteo Negri | Matt Post | Lucia Specia | Karin Verspoor | Jörg Tiedemann | Marco Turchi
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Findings of the 2016 Conference on Machine Translation
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Yvette Graham | Barry Haddow | Matthias Huck | Antonio Jimeno Yepes | Philipp Koehn | Varvara Logacheva | Christof Monz | Matteo Negri | Aurélie Névéol | Mariana Neves | Martin Popel | Matt Post | Raphael Rubino | Carolina Scarton | Lucia Specia | Marco Turchi | Karin Verspoor | Marcos Zampieri
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Results of the WMT16 Metrics Shared Task
Ondřej Bojar | Yvette Graham | Amir Kamran | Miloš Stanojević
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Results of the WMT16 Tuning Shared Task
Bushra Jawaid | Amir Kamran | Miloš Stanojević | Ondřej Bojar
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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

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CUNI-LMU Submissions in WMT2016: Chimera Constrained and Beaten
Aleš Tamchyna | Roman Sudarikov | Ondřej Bojar | Alexander Fraser
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Edinburgh’s Statistical Machine Translation Systems for WMT16
Philip Williams | Rico Sennrich | Maria Nădejde | Matthias Huck | Barry Haddow | Ondřej Bojar
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Dictionary-based Domain Adaptation of MT Systems without Retraining
Rudolf Rosa | Roman Sudarikov | Michal Novák | Martin Popel | Ondřej Bojar
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Particle Swarm Optimization Submission for WMT16 Tuning Task
Viktor Kocur | Ondřej Bojar
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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CUNI System for WMT16 Automatic Post-Editing and Multimodal Translation Tasks
Jindřich Libovický | Jindřich Helcl | Marek Tlustý | Ondřej Bojar | Pavel Pecina
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Using Term Position Similarity and Language Modeling for Bilingual Document Alignment
Thanh C. Le | Hoa Trong Vu | Jonathan Oberländer | Ondřej Bojar
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Bilingual Embeddings and Word Alignments for Translation Quality Estimation
Amal Abdelsalam | Ondřej Bojar | Samhaa El-Beltagy
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Pivoting Methods and Data for Czech-Vietnamese Translation via English
Duc Tam Hoang | Ondrej Bojar
Proceedings of the 19th Annual Conference of the European Association for Machine Translation

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Enriching Source for English-to-Urdu Machine Translation
Bushra Jawaid | Amir Kamran | Ondřej Bojar
Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)

This paper focuses on the generation of case markers for free word order languages that use case markers as phrasal clitics for marking the relationship between the dependent-noun and its head. The generation of such clitics becomes essential task especially when translating from fixed word order languages where syntactic relations are identified by the positions of the dependent-nouns. To address the problem of missing markers on source-side, artificial markers are added in source to improve alignments with its target counterparts. Up to 1 BLEU point increase is observed over the baseline on different test sets for English-to-Urdu.

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Verb sense disambiguation in Machine Translation
Roman Sudarikov | Ondřej Dušek | Martin Holub | Ondřej Bojar | Vincent Kríž
Proceedings of the Sixth Workshop on Hybrid Approaches to Translation (HyTra6)

We describe experiments in Machine Translation using word sense disambiguation (WSD) information. This work focuses on WSD in verbs, based on two different approaches – verbal patterns based on corpus pattern analysis and verbal word senses from valency frames. We evaluate several options of using verb senses in the source-language sentences as an additional factor for the Moses statistical machine translation system. Our results show a statistically significant translation quality improvement in terms of the BLEU metric for the valency frames approach, but in manual evaluation, both WSD methods bring improvements.

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Moses & Treex Hybrid MT Systems Bestiary
Rudolf Rosa | Martin Popel | Ondřej Bojar | David Mareček | Ondřej Dušek
Proceedings of the 2nd Deep Machine Translation Workshop

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TectoMT – a deep linguistic core of the combined Cimera MT system
Martin Popel | Roman Sudarikov | Ondřej Bojar | Rudolf Rosa | Jan Hajič
Proceedings of the 19th Annual Conference of the European Association for Machine Translation: Projects/Products

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Target-Side Context for Discriminative Models in Statistical Machine Translation
Aleš Tamchyna | Alexander Fraser | Ondřej Bojar | Marcin Junczys-Dowmunt
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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HUME: Human UCCA-Based Evaluation of Machine Translation
Alexandra Birch | Omri Abend | Ondřej Bojar | Barry Haddow
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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TeamUFAL: WSD+EL as Document Retrieval
Petr Fanta | Roman Sudarikov | Ondřej Bojar
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Proceedings of the Tenth Workshop on Statistical Machine Translation
Ondřej Bojar | Rajan Chatterjee | Christian Federmann | Barry Haddow | Chris Hokamp | Matthias Huck | Varvara Logacheva | Pavel Pecina
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Findings of the 2015 Workshop on Statistical Machine Translation
Ondřej Bojar | Rajen Chatterjee | Christian Federmann | Barry Haddow | Matthias Huck | Chris Hokamp | Philipp Koehn | Varvara Logacheva | Christof Monz | Matteo Negri | Matt Post | Carolina Scarton | Lucia Specia | Marco Turchi
Proceedings of the Tenth Workshop on Statistical Machine Translation

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CUNI in WMT15: Chimera Strikes Again
Ondřej Bojar | Aleš Tamchyna
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Results of the WMT15 Metrics Shared Task
Miloš Stanojević | Amir Kamran | Philipp Koehn | Ondřej Bojar
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Results of the WMT15 Tuning Shared Task
Miloš Stanojević | Amir Kamran | Ondřej Bojar
Proceedings of the Tenth Workshop on Statistical Machine Translation

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What a Transfer-Based System Brings to the Combination with PBMT
Aleš Tamchyna | Ondřej Bojar
Proceedings of the Fourth Workshop on Hybrid Approaches to Translation (HyTra)

2014

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Twitter Crowd Translation – design and objectives
Eduard Šubert | Ondřej Bojar
Proceedings of Translating and the Computer 36

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Not an Interlingua, But Close: Comparison of English AMRs to Chinese and Czech
Nianwen Xue | Ondřej Bojar | Jan Hajič | Martha Palmer | Zdeňka Urešová | Xiuhong Zhang
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Abstract Meaning Representations (AMRs) are rooted, directional and labeled graphs that abstract away from morpho-syntactic idiosyncrasies such as word category (verbs and nouns), word order, and function words (determiners, some prepositions). Because these syntactic idiosyncrasies account for many of the cross-lingual differences, it would be interesting to see if this representation can serve, e.g., as a useful, minimally divergent transfer layer in machine translation. To answer this question, we have translated 100 English sentences that have existing AMRs into Chinese and Czech to create AMRs for them. A cross-linguistic comparison of English to Chinese and Czech AMRs reveals both cases where the AMRs for the language pairs align well structurally and cases of linguistic divergence. We found that the level of compatibility of AMR between English and Chinese is higher than between English and Czech. We believe this kind of comparison is beneficial to further refining the annotation standards for each of the three languages and will lead to more compatible annotation guidelines between the languages.

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A Tagged Corpus and a Tagger for Urdu
Bushra Jawaid | Amir Kamran | Ondřej Bojar
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, we describe a release of a sizeable monolingual Urdu corpus automatically tagged with part-of-speech tags. We extend the work of Jawaid and Bojar (2012) who use three different taggers and then apply a voting scheme to disambiguate among the different choices suggested by each tagger. We run this complex ensemble on a large monolingual corpus and release the tagged corpus. Additionally, we use this data to train a single standalone tagger which will hopefully significantly simplify Urdu processing. The standalone tagger obtains the accuracy of 88.74% on test data.

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Two-Step Machine Translation with Lattices
Bushra Jawaid | Ondřej Bojar
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The idea of two-step machine translation was introduced to divide the complexity of the search space into two independent steps: (1) lexical translation and reordering, and (2) conjugation and declination in the target language. In this paper, we extend the two-step machine translation structure by replacing state-of-the-art phrase-based machine translation with the hierarchical machine translation in the 1st step. We further extend the fixed string-based input format of the 2nd step with word lattices (Dyer et al., 2008); this provides the 2nd step with the opportunity to choose among a sample of possible reorderings instead of relying on the single best one as produced by the 1st step.

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HindEnCorp - Hindi-English and Hindi-only Corpus for Machine Translation
Ondřej Bojar | Vojtěch Diatka | Pavel Rychlý | Pavel Straňák | Vít Suchomel | Aleš Tamchyna | Daniel Zeman
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present HindEnCorp, a parallel corpus of Hindi and English, and HindMonoCorp, a monolingual corpus of Hindi in their release version 0.5. Both corpora were collected from web sources and preprocessed primarily for the training of statistical machine translation systems. HindEnCorp consists of 274k parallel sentences (3.9 million Hindi and 3.8 million English tokens). HindMonoCorp amounts to 787 million tokens in 44 million sentences. Both the corpora are freely available for non-commercial research and their preliminary release has been used by numerous participants of the WMT 2014 shared translation task.

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Proceedings of the Ninth Workshop on Statistical Machine Translation
Ondřej Bojar | Christian Buck | Christian Federmann | Barry Haddow | Philipp Koehn | Christof Monz | Matt Post | Lucia Specia
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Findings of the 2014 Workshop on Statistical Machine Translation
Ondřej Bojar | Christian Buck | Christian Federmann | Barry Haddow | Philipp Koehn | Johannes Leveling | Christof Monz | Pavel Pecina | Matt Post | Herve Saint-Amand | Radu Soricut | Lucia Specia | Aleš Tamchyna
Proceedings of the Ninth Workshop on Statistical Machine Translation

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CUNI in WMT14: Chimera Still Awaits Bellerophon
Aleš Tamchyna | Martin Popel | Rudolf Rosa | Ondřej Bojar
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Results of the WMT14 Metrics Shared Task
Matouš Macháček | Ondřej Bojar
Proceedings of the Ninth Workshop on Statistical Machine Translation

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English to Urdu Statistical Machine Translation: Establishing a Baseline
Bushra Jawaid | Amir Kamran | Ondřej Bojar
Proceedings of the Fifth Workshop on South and Southeast Asian Natural Language Processing

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Comparing Czech and English AMRs
Jan Hajič | Ondřej Bojar | Zdeňka Urešová
Proceedings of Workshop on Lexical and Grammatical Resources for Language Processing

2013

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Proceedings of the Eighth Workshop on Statistical Machine Translation
Ondrej Bojar | Christian Buck | Chris Callison-Burch | Barry Haddow | Philipp Koehn | Christof Monz | Matt Post | Herve Saint-Amand | Radu Soricut | Lucia Specia
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Findings of the 2013 Workshop on Statistical Machine Translation
Ondřej Bojar | Christian Buck | Chris Callison-Burch | Christian Federmann | Barry Haddow | Philipp Koehn | Christof Monz | Matt Post | Radu Soricut | Lucia Specia
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Results of the WMT13 Metrics Shared Task
Matouš Macháček | Ondřej Bojar
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Chimera – Three Heads for English-to-Czech Translation
Ondřej Bojar | Rudolf Rosa | Aleš Tamchyna
Proceedings of the Eighth Workshop on Statistical Machine Translation

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PhraseFix: Statistical Post-Editing of TectoMT
Petra Galuščáková | Martin Popel | Ondřej Bojar
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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Automatic MT Error Analysis: Hjerson Helping Addicter
Jan Berka | Ondřej Bojar | Mark Fishel | Maja Popović | Daniel Zeman
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We present a complex, open source tool for detailed machine translation error analysis providing the user with automatic error detection and classification, several monolingual alignment algorithms as well as with training and test corpus browsing. The tool is the result of a merge of automatic error detection and classification of Hjerson (Popović, 2011) and Addicter (Zeman et al., 2011) into the pipeline and web visualization of Addicter. It classifies errors into categories similar to those of Vilar et al. (2006), such as: morphological, reordering, missing words, extra words and lexical errors. The graphical user interface shows alignments in both training corpus and test data; the different classes of errors are colored. Also, the summary of errors can be displayed to provide an overall view of the MT system's weaknesses. The tool was developed in Linux, but it was tested on Windows too.

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Terra: a Collection of Translation Error-Annotated Corpora
Mark Fishel | Ondřej Bojar | Maja Popović
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Recently the first methods of automatic diagnostics of machine translation have emerged; since this area of research is relatively young, the efforts are not coordinated. We present a collection of translation error-annotated corpora, consisting of automatically produced translations and their detailed manual translation error analysis. Using the collected corpora we evaluate the available state-of-the-art methods of MT diagnostics and assess, how well the methods perform, how they compare to each other and whether they can be useful in practice.

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Announcing Prague Czech-English Dependency Treebank 2.0
Jan Hajič | Eva Hajičová | Jarmila Panevová | Petr Sgall | Ondřej Bojar | Silvie Cinková | Eva Fučíková | Marie Mikulová | Petr Pajas | Jan Popelka | Jiří Semecký | Jana Šindlerová | Jan Štěpánek | Josef Toman | Zdeňka Urešová | Zdeněk Žabokrtský
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

We introduce a substantial update of the Prague Czech-English Dependency Treebank, a parallel corpus manually annotated at the deep syntactic layer of linguistic representation. The English part consists of the Wall Street Journal (WSJ) section of the Penn Treebank. The Czech part was translated from the English source sentence by sentence. This paper gives a high level overview of the underlying linguistic theory (the so-called tectogrammatical annotation) with some details of the most important features like valency annotation, ellipsis reconstruction or coreference.

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The Joy of Parallelism with CzEng 1.0
Ondřej Bojar | Zdeněk Žabokrtský | Ondřej Dušek | Petra Galuščáková | Martin Majliš | David Mareček | Jiří Maršík | Michal Novák | Martin Popel | Aleš Tamchyna
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

CzEng 1.0 is an updated release of our Czech-English parallel corpus, freely available for non-commercial research or educational purposes. In this release, we approximately doubled the corpus size, reaching 15 million sentence pairs (about 200 million tokens per language). More importantly, we carefully filtered the data to reduce the amount of non-matching sentence pairs. CzEng 1.0 is automatically aligned at the level of sentences as well as words. We provide not only the plain text representation, but also automatic morphological tags, surface syntactic as well as deep syntactic dependency parse trees and automatic co-reference links in both English and Czech. This paper describes key properties of the released resource including the distribution of text domains, the corpus data formats, and a toolkit to handle the provided rich annotation. We also summarize the procedure of the rich annotation (incl. co-reference resolution) and of the automatic filtering. Finally, we provide some suggestions on exploiting such an automatically annotated sentence-parallel corpus.

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TerrorCat: a Translation Error Categorization-based MT Quality Metric
Mark Fishel | Rico Sennrich | Maja Popović | Ondřej Bojar
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Probes in a Taxonomy of Factored Phrase-Based Models
Ondřej Bojar | Bushra Jawaid | Amir Kamran
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Selecting Data for English-to-Czech Machine Translation
Aleš Tamchyna | Petra Galuščáková | Amir Kamran | Miloš Stanojević | Ondřej Bojar
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Towards a Predicate-Argument Evaluation for MT
Ondřej Bojar | Dekai Wu
Proceedings of the Sixth Workshop on Syntax, Semantics and Structure in Statistical Translation

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Tagger Voting for Urdu
Bushra Jawaid | Ondřej Bojar
Proceedings of the 3rd Workshop on South and Southeast Asian Natural Language Processing

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Morphological Processing for English-Tamil Statistical Machine Translation
Loganathan Ramasamy | Ondřej Bojar | Zdeněk Žabokrtský
Proceedings of the Workshop on Machine Translation and Parsing in Indian Languages

2011

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A Grain of Salt for the WMT Manual Evaluation
Ondřej Bojar | Miloš Ercegovčević | Martin Popel | Omar Zaidan
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Approximating a Deep-Syntactic Metric for MT Evaluation and Tuning
Matouš Macháček | Ondřej Bojar
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Improving Translation Model by Monolingual Data
Ondřej Bojar | Aleš Tamchyna
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Two-step translation with grammatical post-processing
David Mareček | Rudolf Rosa | Petra Galuščáková | Ondřej Bojar
Proceedings of the Sixth Workshop on Statistical Machine Translation

2010

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Building a Bilingual ValLex Using Treebank Token Alignment: First Observations
Jana Šindlerová | Ondřej Bojar
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We explore the potential and limitations of a concept of building a bilingual valency lexicon based on the alignment of nodes in a parallel treebank. Our aim is to build an electronic Czech->English Valency Lexicon by collecting equivalences from bilingual treebank data and storing them in two already existing electronic valency lexicons, PDT-VALLEX and Engvallex. For this task a special annotation interface has been built upon the TrEd editor, allowing quick and easy collecting of frame equivalences in either of the source lexicons. The issues encountered so far include limitations of technical character, theory-dependent limitations and limitations concerning the achievable degree of quality of human annotation. The issues of special interest for both linguists and MT specialists involved in the project include linguistically motivated non-balance between the frame equivalents, either in number or in type of valency participants. The first phases of annotation so far attest the assumption that there is a unique correspondence between the functors of the translation-equivalent frames. Also, hardly any linguistically significant non-balance between the frames has been found, which is partly promising considering the linguistic theory used and partly caused by little stylistic variety of the annotated corpus texts.

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Evaluating Utility of Data Sources in a Large Parallel Czech-English Corpus CzEng 0.9
Ondřej Bojar | Adam Liška | Zdeněk Žabokrtský
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

CzEng 0.9 is the third release of a large parallel corpus of Czech and English. For the current release, CzEng was extended by significant amount of texts from various types of sources, including parallel web pages, electronically available books and subtitles. This paper describes and evaluates filtering techniques employed in the process in order to avoid misaligned or otherwise damaged parallel sentences in the collection. We estimate the precision and recall of two sets of filters. The first set was used to process the data before their inclusion into CzEng. The filters from the second set were newly created to improve the filtering process for future releases of CzEng. Given the overall amount and variance of sources of the data, our experiments illustrate the utility of parallel data sources with respect to extractable parallel segments. As a similar behaviour can be expected for other language pairs, our results can be interpreted as guidelines indicating which sources should other researchers exploit first.

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Data Issues in English-to-Hindi Machine Translation
Ondřej Bojar | Pavel Straňák | Daniel Zeman
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Statistical machine translation to morphologically richer languages is a challenging task and more so if the source and target languages differ in word order. Current state-of-the-art MT systems thus deliver mediocre results. Adding more parallel data often helps improve the results; if it doesn't, it may be caused by various problems such as different domains, bad alignment or noise in the new data. In this paper we evaluate the English-to-Hindi MT task from this data perspective. We discuss several available parallel data sources and provide cross-evaluation results on their combinations using two freely available statistical MT systems. We demonstrate various problems encountered in the data and describe automatic methods of data cleaning and normalization. We also show that the contents of two independently distributed data sets can unexpectedly overlap, which negatively affects translation quality. Together with the error analysis, we also present a new tool for viewing aligned corpora, which makes it easier to detect difficult parts in the data even for a developer not speaking the target language.

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Tackling Sparse Data Issue in Machine Translation Evaluation
Ondřej Bojar | Kamil Kos | David Mareček
Proceedings of the ACL 2010 Conference Short Papers

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2010 Failures in English-Czech Phrase-Based MT
Ondřej Bojar | Kamil Kos
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

2009

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Computer-aided translation backed by machine translation
Ondřej Odcházal | Ondřej Bojar
Proceedings of Translating and the Computer 31

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English-Czech MT in 2008
Ondřej Bojar | David Mareček | Václav Novák | Martin Popel | Jan Ptáček | Jan Rouš | Zdeněk Žabokrtský
Proceedings of the Fourth Workshop on Statistical Machine Translation

2008

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CzEng 0.7: Parallel Corpus with Community-Supplied Translations
Ondřej Bojar | Miroslav Janíček | Zdeněk Žabokrtský | Pavel Češka | Peter Beňa
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper describes CzEng 0.7, a new release of Czech-English parallel corpus freely available for research and educational purposes. We provide basic statistics of the corpus and focus on data produced by a community of volunteers. Anonymous contributors manually correct the output of a machine translation (MT) system, generating on average 2000 sentences a month, 70% of which are indeed correct translations. We compare the utility of community-supplied and of professionally translated training data for a baseline English-to-Czech MT system.

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Phrase-Based and Deep Syntactic English-to-Czech Statistical Machine Translation
Ondřej Bojar | Jan Hajič
Proceedings of the Third Workshop on Statistical Machine Translation

2007

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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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English-to-Czech Factored Machine Translation
Ondřej Bojar
Proceedings of the Second Workshop on Statistical Machine Translation

2006

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Czech-English Word Alignment
Ondřej Bojar | Magdelena Prokopová
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We describe an experiment with Czech-English word alignment. Half a thousand sentences were manually annotated by two annotators in parallel and the most frequent reasons for disagreement are described. We evaluate the accuracy of GIZA++ alignment toolkit on the data and identify that lemmatization of the Czech part can reduce alignment error to a half. Furthermore we document that about 38% of tokens difficult for GIZA++ were difficult for humans already.

2005

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An MT System Recycled
Ondřej Bojar | Petr Homola | Vladislav Kuboň
Proceedings of Machine Translation Summit X: Posters

This paper describes an attempt to recycle parts of the Czech-to-Russian machine translation system (MT) in the new Czech-to-English MT system. The paper describes the overall architecture of the new system and the details of the modules which have been added. A special attention is paid to the problem of named entity recognition and to the method of automatic acquisition of lexico-syntactic information for the bilingual dictionary of the system.

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Problems of Reusing an Existing MT System
Ondřej Bojar | Petr Homola | Vladislav Kuboň
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts

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