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

机译：2017年DiscoMT跨语言代词预测共同任务的发现

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摘要

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 lem-matized 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 all participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin.
机译：我们描述了DiscoMT 2017跨语言代词预测共享任务的设计，设置和评估结果。任务要求参与者在句子的上下文中根据给定的源语言代词来预测目标语言代词。我们进一步提供了源语言句子的lem-matizing目标语言人工翻译，以及源句子单词和目标语言引词之间的自动单词对齐。该任务的目的是针对每个目标语言代词占位符，使用可以从整个文档中提取的任何类型的信息，预测应从一小组封闭的小类中替换该词的单词。我们提供了四个子任务，每个子任务针对不同的语言对和翻译方向：英语到法语，英语到德语，德语到英语和西班牙语到英语。五支团队参加了这项共同任务，为所有语言对提交了论文。评估结果表明，所有参赛团队的表现均优于两个强大的基于n元语法的基于语言模型的基线系统。

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