Incorporating syntactic information in Neural Machine Translation models is a
method to compensate their requirement for a large amount of parallel training
text, especially for low-resource language pairs. Previous works on using
syntactic information provided by (inevitably error-prone) parsers has been
promising. In this paper, we propose a forest-to-sequence Attentional Neural
Machine Translation model to make use of exponentially many parse trees of the
source sentence to compensate for the parser errors. Our method represents the
collection of parse trees as a packed forest, and learns a neural attentional
transduction model from the forest to the target sentence. Experiments on
English to German, Chinese and Persian translation show the superiority of our
method over the tree-to-sequence and vanilla sequence-to-sequence neural
translation models.

本文提出了一种基于注意力机制的神经网络机器翻译模型，在源语句的因果关系树中学习一个神经注意力转导模型，并使用句法信息来弥补语料库相对匮乏的问题。实验证明，相比于其他神经翻译模型，该模型在英语 - 德语、英语 - 中文和英语 - 波斯语翻译任务中有优异的表现。