Most pronouns are referring expressions, computers need to resolve what do the pronouns refer to, and there are divergences on pronoun usage across languages. Thus, dealing with these divergences and translating pronouns is a challenge in machine translation. Mentions are referring candidates of pronouns and have closer relations with pronouns compared to general tokens. We assume that extracting additional mention features can help pronoun translation. Therefore, we introduce an additional mention attention module in the decoder to pay extra attention to source mentions but not non-mention tokens. Our mention attention module not only extracts features from source mentions, but also considers target-side context which benefits pronoun translation. In addition, we also introduce two mention classifiers to train models to recognize mentions, whose outputs guide the mention attention. We conduct experiments on the WMT17 English-German translation task, and evaluate our models on general translation and pronoun translation, using BLEU, APT, and contrastive evaluation metrics. Our proposed model outperforms the baseline Transformer model in terms of APT and BLEU scores, this confirms our hypothesis that we can improve pronoun translation by paying additional attention to source mentions, and shows that our introduced additional modules do not have negative effect on the general translation quality.

翻译：大多数代词属于指代表达，计算机需要解析代词所指代的内容，而不同语言在代词使用上存在差异。因此，处理这些差异并准确翻译代词是机器翻译领域的一大挑战。指代项作为代词的潜在指代候选，与代词之间存在比普通词元更紧密的关联。我们假设提取额外的指代项特征有助于改善代词翻译。为此，我们在解码器中引入了一个额外的指代注意力模块，使其能够对源语言指代项（而非非指代词元）施加额外关注。该指代注意力模块不仅从源语言指代项中提取特征，同时考虑目标端上下文信息以提升代词翻译质量。此外，我们还引入了两个指代分类器来训练模型识别指代项，其输出用于指导指代注意力模块。我们在WMT17英德翻译任务上进行了实验，并采用BLEU、APT及对比评估指标对模型的通用翻译能力和代词翻译性能进行评估。实验结果表明，我们提出的模型在APT和BLEU分数上均优于基准Transformer模型，这验证了我们的假设——通过对源语言指代项施加额外注意力可以改进代词翻译，同时证明所引入的附加模块不会对通用翻译质量产生负面影响。