Neural machine translation requires large amounts of parallel training text
to learn a reasonable-quality translation model. This is particularly
inconvenient for language pairs for which enough parallel text is not
available. In this paper, we use monolingual linguistic resources in the source
side to address this challenging problem based on a multi-task learning
approach. More specifically, we scaffold the machine translation task on
auxiliary tasks including semantic parsing, syntactic parsing, and named-entity
recognition. This effectively injects semantic and/or syntactic knowledge into
the translation model, which would otherwise require a large amount of training
bitext. We empirically evaluate and show the effectiveness of our multi-task
learning approach on three translation tasks: English-to-French,
English-to-Farsi, and English-to-Vietnamese.

本文提出了一种基于多任务学习方法，利用源端的单语言语言资源来解决神经机器翻译中由于缺少平行文本造成模型质量差的问题，并采用语义分析、句法分析和命名实体识别等辅助任务以将语义和 / 或句法知识注入到翻译模型中，实现了在英法、英波斯和英越三种翻译任务上的有效性验证。