Correspondences between Czech and English        Coreferential Expressions

Novák, Michal; Nedoluzhko, Anna

doi:10.4000/discours.9058

Cited by 9 publications

(9 citation statements)

References 12 publications

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“…In contrast, the occurrences of su that should have been translated with human pronouns (his, her) are better translated by the CM. Notably, despite its ambiguity, ment, assuming the availability of parse trees, has been proposed by Novák and Nedoluzhko (2015).…”

Section: Results With Corzu Antecedentsmentioning

confidence: 99%

Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities

Luong¹,

Popescu-Belis²,

Gonzales

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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We implement a fully probabilistic model to combine the hypotheses of a Spanish anaphora resolution system with those of a Spanish-English machine translation system. The probabilities over antecedents are converted into probabilities for the features of translated pronouns, and are integrated with phrase-based MT using an additional translation model for pronouns. The system improves the translation of several Spanish personal and possessive pronouns into English, by solving translation divergencies such as ella → she | it or su → his | her | its | their. On a test set with 2,286 pronouns, a baseline system correctly translates 1,055 of them, while ours improves this by 41. Moreover, with oracle antecedents, possessives are translated with an accuracy of 83%.

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Section: Results With Corzu Antecedentsmentioning

confidence: 99%

Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities

Luong¹,

Popescu-Belis²,

Gonzales

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 2

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“…These mentions are realised by a vari-ety of linguistic devices such as pronouns, nominal phrases (NPs) and other linguistic means. As languages differ in the range of such linguistic means (Lapshinova-Koltunski et al, 2019;Kunz and Lapshinova-Koltunski, 2015;Novák and Nedoluzhko, 2015;Kunz and Steiner, 2012) and in their contextual restrictions (Kunz et al, 2017a), these differences give rise to problems that may result in incoherent (automatic) translations. We focus on coreference chains in English-German translations belonging to two different genres.…”

Section: Introductionmentioning

confidence: 99%

Analysing Coreference in Transformer Outputs

Lapshinova-Koltunski

España-Bonet

Genabith

2019

Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)

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We analyse coreference phenomena in three neural machine translation systems trained with different data settings with or without access to explicit intra-and cross-sentential anaphoric information. We compare system performance on two different genres: news and TED talks. To do this, we manually annotate (the possibly incorrect) coreference chains in the MT outputs and evaluate the coreference chain translations. We define an error typology that aims to go further than pronoun translation adequacy and includes types such as incorrect word selection or missing words. The features of coreference chains in automatic translations are also compared to those of the source texts and human translations. The analysis shows stronger potential translationese effects in machine translated outputs than in human translations.

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“…With our corpus such phenomena can be directly compared across languages. This work thus follows on the comparative analysis that has been previously conducted on coreferential expressions in English and Czech (Novák and Nedoluzhko, 2015) and reflexive possessives in English, Czech and Russian (Nedoluzhko et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

PAWS: A Multi-lingual Parallel Treebank with Anaphoric Relations

Nedoluzhko¹,

Novák²,

Ogrodniczuk³

2018

Proceedings of the First Workshop on Computational Models Of Reference, Anaphora and Coreference

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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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Correspondences between Czech and English Coreferential Expressions

Cited by 9 publications

References 12 publications

Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities

Machine Translation of Spanish Personal and Possessive Pronouns Using Anaphora Probabilities

Analysing Coreference in Transformer Outputs

PAWS: A Multi-lingual Parallel Treebank with Anaphoric Relations

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