Michal Novák


2022

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CorefUD 1.0: Coreference Meets Universal Dependencies
Anna Nedoluzhko | Michal Novák | Martin Popel | Zdeněk Žabokrtský | Amir Zeldes | Daniel Zeman
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Recent advances in standardization for annotated language resources have led to successful large scale efforts, such as the Universal Dependencies (UD) project for multilingual syntactically annotated data. By comparison, the important task of coreference resolution, which clusters multiple mentions of entities in a text, has yet to be standardized in terms of data formats or annotation guidelines. In this paper we present CorefUD, a multilingual collection of corpora and a standardized format for coreference resolution, compatible with morphosyntactic annotations in the UD framework and including facilities for related tasks such as named entity recognition, which forms a first step in the direction of convergence for coreference resolution across languages.

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Findings of the Shared Task on Multilingual Coreference Resolution
Zdeněk Žabokrtský | Miloslav Konopík | Anna Nedoluzhko | Michal Novák | Maciej Ogrodniczuk | Martin Popel | Ondřej Pražák | Jakub Sido | Daniel Zeman | Yilun Zhu
Proceedings of the CRAC 2022 Shared Task on Multilingual Coreference Resolution

This paper presents an overview of the shared task on multilingual coreference resolution associated with the CRAC 2022 workshop. Shared task participants were supposed to develop trainable systems capable of identifying mentions and clustering them according to identity coreference. The public edition of CorefUD 1.0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data. The CoNLL score used in previous coreference-oriented shared tasks was used as the main evaluation metric. There were 8 coreference prediction systems submitted by 5 participating teams; in addition, there was a competitive Transformer-based baseline system provided by the organizers at the beginning of the shared task. The winner system outperformed the baseline by 12 percentage points (in terms of the CoNLL scores averaged across all datasets for individual languages).

2021

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CUNI systems for WMT21: Multilingual Low-Resource Translation for Indo-European Languages Shared Task
Josef Jon | Michal Novák | João Paulo Aires | Dusan Varis | Ondřej Bojar
Proceedings of the Sixth Conference on Machine Translation

This paper describes Charles University sub-mission for Terminology translation shared task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high overall translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database.

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CUNI Systems for WMT21: Terminology Translation Shared Task
Josef Jon | Michal Novák | João Paulo Aires | Dusan Varis | Ondřej Bojar
Proceedings of the Sixth Conference on Machine Translation

This paper describes Charles University sub-mission for Terminology translation Shared Task at WMT21. The objective of this task is to design a system which translates certain terms based on a provided terminology database, while preserving high overall translation quality. We competed in English-French language pair. Our approach is based on providing the desired translations alongside the input sentence and training the model to use these provided terms. We lemmatize the terms both during the training and inference, to allow the model to learn how to produce correct surface forms of the words, when they differ from the forms provided in the terminology database. Our submission ranked second in Exact Match metric which evaluates the ability of the model to produce desired terms in the translation.

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Do UD Trees Match Mention Spans in Coreference Annotations?
Martin Popel | Zdeněk Žabokrtský | Anna Nedoluzhko | Michal Novák | Daniel Zeman
Findings of the Association for Computational Linguistics: EMNLP 2021

One can find dozens of data resources for various languages in which coreference - a relation between two or more expressions that refer to the same real-world entity - is manually annotated. One could also assume that such expressions usually constitute syntactically meaningful units; however, mention spans have been annotated simply by delimiting token intervals in most coreference projects, i.e., independently of any syntactic representation. We argue that it could be advantageous to make syntactic and coreference annotations convergent in the long term. We present a pilot empirical study focused on matches and mismatches between hand-annotated linear mention spans and automatically parsed syntactic trees that follow Universal Dependencies conventions. The study covers 9 datasets for 8 different languages.

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Is one head enough? Mention heads in coreference annotations compared with UD-style heads
Anna Nedoluzhko | Michal Novák | Martin Popel | Zdeněk Žabokrtský | Daniel Zeman
Proceedings of the Sixth International Conference on Dependency Linguistics (Depling, SyntaxFest 2021)

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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.

2019

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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.

2018

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PAWS: A Multi-lingual Parallel Treebank with Anaphoric Relations
Anna Nedoluzhko | Michal Novák | Maciej Ogrodniczuk
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

We present PAWS, a multi-lingual parallel treebank with coreference annotation. It consists of English texts from the Wall Street Journal translated into Czech, Russian and Polish. In addition, the texts are syntactically parsed and word-aligned. PAWS is based on PCEDT 2.0 and continues the tradition of multilingual treebanks with coreference annotation. The paper focuses on the coreference annotation in PAWS and its language-specific differences. PAWS offers linguistic material that can be further leveraged in cross-lingual studies, especially on coreference.

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A Fine-grained Large-scale Analysis of Coreference Projection
Michal Novák
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

We perform a fine-grained large-scale analysis of coreference projection. By projecting gold coreference from Czech to English and vice versa on Prague Czech-English Dependency Treebank 2.0 Coref, we set an upper bound of a proposed projection approach for these two languages. We undertake a detailed thorough analysis that combines the analysis of projection’s subtasks with analysis of performance on individual mention types. The findings are accompanied with examples from the corpus.

2017

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Introducing EVALD – Software Applications for Automatic Evaluation of Discourse in Czech
Kateřina Rysová | Magdaléna Rysová | Jiří Mírovský | Michal Novák
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

In the paper, we introduce two software applications for automatic evaluation of coherence in Czech texts called EVALD – Evaluator of Discourse. The first one – EVALD 1.0 – evaluates texts written by native speakers of Czech on a five-step scale commonly used at Czech schools (grade 1 is the best, grade 5 is the worst). The second application is EVALD 1.0 for Foreigners assessing texts by non-native speakers of Czech using six-step scale (A1–C2) according to CEFR. Both appli-cations are available online at https://lindat.mff.cuni.cz/services/evald-foreign/.

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Projection-based Coreference Resolution Using Deep Syntax
Michal Novák | Anna Nedoluzhko | Zdeněk Žabokrtský
Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

The paper describes the system for coreference resolution in German and Russian, trained exclusively on coreference relations project ed through a parallel corpus from English. The resolver operates on the level of deep syntax and makes use of multiple specialized models. It achieves 32 and 22 points in terms of CoNLL score for Russian and German, respectively. Analysis of the evaluation results show that the resolver for Russian is able to preserve 66% of the English resolver’s quality in terms of CoNLL score. The system was submitted to the Closed track of the CORBON 2017 Shared task.

2016

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Coreference in Prague Czech-English Dependency Treebank
Anna Nedoluzhko | Michal Novák | Silvie Cinková | Marie Mikulová | Jiří Mírovský
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present coreference annotation on parallel Czech-English texts of the Prague Czech-English Dependency Treebank (PCEDT). The paper describes innovations made to PCEDT 2.0 concerning coreference, as well as coreference information already present there. We characterize the coreference annotation scheme, give the statistics and compare our annotation with the coreference annotation in Ontonotes and Prague Dependency Treebank for Czech. We also present the experiments made using this corpus to improve the alignment of coreferential expressions, which helps us to collect better statistics of correspondences between types of coreferential relations in Czech and English. The corpus released as PCEDT 2.0 Coref is publicly available.

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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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Pronoun Prediction with Linguistic Features and Example Weighing
Michal Novák
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

2015

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Comparison of Coreference Resolvers for Deep Syntax Translation
Michal Novák | Dieke Oele | Gertjan van Noord
Proceedings of the Second Workshop on Discourse in Machine Translation

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New Language Pairs in TectoMT
Ondřej Dušek | Luís Gomes | Michal Novák | Martin Popel | Rudolf Rosa
Proceedings of the Tenth Workshop on Statistical Machine Translation

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Translation Model Interpolation for Domain Adaptation in TectoMT
Rudolf Rosa | Ondřej Dušek | Michal Novák | Martin Popel
Proceedings of the 1st Deep Machine Translation Workshop

2014

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Cross-lingual Coreference Resolution of Pronouns
Michal Novák | Zdeněk Žabokrtský
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Machine Translation of Medical Texts in the Khresmoi Project
Ondřej Dušek | Jan Hajič | Jaroslava Hlaváčová | Michal Novák | Pavel Pecina | Rudolf Rosa | Aleš Tamchyna | Zdeňka Urešová | Daniel Zeman
Proceedings of the Ninth Workshop on Statistical Machine Translation

2013

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Translation of “It” in a Deep Syntax Framework
Michal Novák | Anna Nedoluzhko | Zdeněk Žabokrtský
Proceedings of the Workshop on Discourse in Machine Translation

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Two Case Studies on Translating Pronouns in a Deep Syntax Framework
Michal Novák | Zdeněk Žabokrtský | Anna Nedoluzhko
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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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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Formemes in English-Czech Deep Syntactic MT
Ondřej Dušek | Zdeněk Žabokrtský | Martin Popel | Martin Majliš | Michal Novák | David Mareček
Proceedings of the Seventh Workshop on Statistical Machine Translation