Grammar as a Foreign Language

Syntactic parsing is a fundamental problem in computational linguistics and natural language processing. Traditional approaches to parsing are highly complex and problem specific. Recently, Sutskever et al. (2014) presented a task-agnostic method for learning to map input sequences to output sequences that achieved strong results on a large scale machine translation problem. In this work, we show that precisely the same sequence-to-sequence method achieves results that are close to state-of-the-art on syntactic constituency parsing, whilst making almost no assumptions about the structure of the problem. To achieve these results we need to mitigate the lack of domain knowledge in the model by providing it with a large amount of automatically parsed data.