@inproceedings{loaiciga-etal-2017-findings,
    title = "Findings of the 2017 {D}isco{MT} Shared Task on Cross-lingual Pronoun Prediction",
    author = {Lo{\'a}iciga, Sharid  and
      Stymne, Sara  and
      Nakov, Preslav  and
      Hardmeier, Christian  and
      Tiedemann, J{\"o}rg  and
      Cettolo, Mauro  and
      Versley, Yannick},
    editor = {Webber, Bonnie  and
      Popescu-Belis, Andrei  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4801",
    doi = "10.18653/v1/W17-4801",
    pages = "1--16",
    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.",
}