  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.
