Zero pronouns (ZPs) are frequently omitted in pro-drop languages, but should
be recalled in non-pro-drop languages. This discourse phenomenon poses a
significant challenge for machine translation (MT) when translating texts from
pro-drop to non-pro-drop languages. In this paper, we propose a unified and
discourse-aware ZP translation approach for neural MT models. Specifically, we
jointly learn to predict and translate ZPs in an end-to-end manner, allowing
both components to interact with each other. In addition, we employ
hierarchical neural networks to exploit discourse-level context, which is
beneficial for ZP prediction and thus translation. Experimental results on both
Chinese-English and Japanese-English data show that our approach significantly
and accumulatively improves both translation performance and ZP prediction
accuracy over not only baseline but also previous works using external ZP
prediction models. Extensive analyses confirm that the performance improvement
comes from the alleviation of different kinds of errors especially caused by
subjective ZPs.

本文提出了一种统一且考虑话语的零代词（Zero pronouns, ZPs）翻译方法，利用神经网络实现对 ZP 的预测和翻译，并采用分层神经网络来利用话语上下文，实验结果表明该方法在中英和日英语料数据上显著提高了机器翻译和 ZP 预测的准确性，尤其是缓解了主观 ZPs 带来的误差。