Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction

By Sharid Loáiciga, Sara Stymne, Preslav Nakov, Christian Hardmeier, Jörg Tiedemann, Mauro Cettolo, Yannick Versley. 2017

22 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Scalable Cross Lingual Pivots to Model Pronoun Gender for Translation (2020, background): “Further, the DiscoMT shared task on pronoun prediction tested multiple language pairs and found that the most difficult language pair by far was Spanish→English and that “[t]his result reflects the difficulty of translating null subjects” (Loáiciga et al., 2017).”
Evaluating Discourse Phenomena in Neural Machine Translation (2017, methodology): “Much of the previous work, mainly in statistical machine translation (SMT), focused on post-edition, particularly for anaphoric pronoun translation (Guillou et al., 2016; Lo´aiciga et al., 2017).”
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments (2018, background): “…coreference relates to the issue of translating anaphoric pronouns and is tackled in several works (Guillou, 2016; Hardmeier and Federico, 2010; Le Nagard and Koehn, 2010) and is the central motivation for the DiscoMT shared task on cross-lingual pronoun prediction (Lo´aiciga et al., 2017).”