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        <title>Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction</title>
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            <title>Proceedings of the Third Workshop on Discourse in Machine Translation</title>
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    <abstract>We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the target-language lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document. We offered four subtasks, each for a different language pair and translation direction: English-to-French, English-to-German, German-to-English, and Spanish-to-English. Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that most participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin.</abstract>
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%0 Conference Proceedings
%T Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction
%A Loáiciga, Sharid
%A Stymne, Sara
%A Nakov, Preslav
%A Hardmeier, Christian
%A Tiedemann, Jörg
%A Cettolo, Mauro
%A Versley, Yannick
%Y Webber, Bonnie
%Y Popescu-Belis, Andrei
%Y Tiedemann, Jörg
%S Proceedings of the Third Workshop on Discourse in Machine Translation
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F loaiciga-etal-2017-findings
%X We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the target-language lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document. We offered four subtasks, each for a different language pair and translation direction: English-to-French, English-to-German, German-to-English, and Spanish-to-English. Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that most participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin.
%R 10.18653/v1/W17-4801
%U https://aclanthology.org/W17-4801/
%U https://doi.org/10.18653/v1/W17-4801
%P 1-16

Markdown (Informal)

[Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction](https://aclanthology.org/W17-4801/) (Loáiciga et al., DiscoMT 2017)

• Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction (Loáiciga et al., DiscoMT 2017)

ACL

• Sharid Loáiciga, Sara Stymne, Preslav Nakov, Christian Hardmeier, Jörg Tiedemann, Mauro Cettolo, and Yannick Versley. 2017. Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction. In Proceedings of the Third Workshop on Discourse in Machine Translation, pages 1–16, Copenhagen, Denmark. Association for Computational Linguistics.