Pronoun Translation and Prediction with or without Coreference Links

Luong, Ngoc Quang; Werlen, Lesly Miculicich; Popescu-Belis, Andréi

doi:10.18653/v1/w15-2513

Cited by 10 publications

(9 citation statements)

References 13 publications

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“…The IDIAP (Luong et al, 2015) and the AUTO-POSTEDIT (Guillou, 2015) submissions were phrase-based, built using the same training and tuning resources and methods as the official baseline. Both adopted a two-pass approach involving an automatic post-editing step to correct the pronoun translations output by the baseline system, and both of them relied on the Stanford anaphora resolution software (Lee et al, 2011).…”

Section: Submitted Systemsmentioning

confidence: 99%

Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation

Hardmeier¹,

Nakov²,

Stymne³

et al. 2015

Proceedings of the Second Workshop on Discourse in Machine Translation

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We describe the design, the evaluation setup, and the results of the DiscoMT 2015 shared task, which included two subtasks, relevant to both the machine translation (MT) and the discourse communities: (i) pronoun-focused translation, a practical MT task, and (ii) cross-lingual pronoun prediction, a classification task that requires no specific MT expertise and is interesting as a machine learning task in its own right. We focused on the English-French language pair, for which MT output is generally of high quality, but has visible issues with pronoun translation due to differences in the pronoun systems of the two languages. Six groups participated in the pronoun-focused translation task and eight groups in the cross-lingual pronoun prediction task.

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Section: Submitted Systemsmentioning

confidence: 99%

Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation

Hardmeier¹,

Nakov²,

Stymne³

et al. 2015

Proceedings of the Second Workshop on Discourse in Machine Translation

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“…• PE: our post-editing system for the translations of it and they generated by a baseline SMT system (Luong et al, 2015), which was the highest scoring system at the DiscoMT 2015 shared task on pronoun-focused translation. It was trained on the DiscoMT 2015 data and tuned on the IWSLT 2010 development data.…”

Section: Results Using Automatic Metricsmentioning

confidence: 99%

Improving Pronoun Translation by Modeling Coreference Uncertainty

Luong¹,

Popescu-Belis²

2016

Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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Information about the antecedents of pronouns is considered essential to solve certain translation divergencies, such as those concerning the English pronoun it when translated into gendered languages, e.g. for French into il, elle, or several other options. However, no machine translation system using anaphora resolution has so far been able to outperform a phrase-based statistical MT baseline. We address here one of the reasons for this failure: the imperfection of automatic anaphora resolution algorithms. Using parallel data, we learn probabilistic correlations between target-side pronouns and the gender and number features of their (uncertain) antecedents, as hypothesized by the Stanford Coreference Resolution system on the source side. We embody these correlations into a secondary translation model, which we invoke upon decoding with the Moses statistical phrase-based MT system. This solution outperforms a deterministic pronoun post-editing system, as well as a statistical MT baseline, on automatic and human evaluation metrics.

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“…The improvement of pronoun translation was only marginal with respect to a baseline SMT system in the 2015 shared task , while the 2016 shared task was only aiming at pronoun prediction given source texts and lemmatized reference translations (Guillou et al, 2016). Some of the best systems developed for these tasks avoided, in fact, the direct use of anaphora resolution (with the exception of Luong et al (2015)). For example, Callin et al (2015) designed a classifier based on a feed-forward neural network, which considered as features the preceding nouns and determiners along with their part-of-speech tags.…”

Section: Coreference-aware Machine Translationmentioning

confidence: 99%

Using Coreference Links to Improve Spanish-to-English Machine Translation

Werlen¹,

Popescu-Belis²

2017

Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

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In this paper, we present a proof-ofconcept of a coreference-aware decoder for document-level machine translation. We consider that better translations should have coreference links that are closer to those in the source text, and implement this criterion in two ways. First, we define a similarity measure between source and target coreference structures, by projecting the target ones onto the source ones, and then reusing existing monolingual coreference metrics. Based on this similarity measure, we re-rank the translation hypotheses of a baseline MT system for each sentence. Alternatively, to address the lack of diversity of mentions among the MT hypotheses, we focus on mention pairs and integrate their coreference scores with MT ones, resulting in post-editing decisions. Experiments with Spanish-to-English MT on the AnCora-ES corpus show that our second approach yields a substantial increase in the accuracy of pronoun translation, while BLEU scores remain constant.

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Pronoun Translation and Prediction with or without Coreference Links

Cited by 10 publications

References 13 publications

Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation

Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation

Improving Pronoun Translation by Modeling Coreference Uncertainty

Using Coreference Links to Improve Spanish-to-English Machine Translation

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