Capturing Dependency Syntax with “deep” Sequential Models

Neural network (“deep learning”) models are taking over machine learning approaches for language by storm. In particular, recurrent neural networks (RNNs), which are flexible non-markovian models of sequential data, were shown to be effective for a variety of language processing tasks. Somewhat surprisingly, these seemingly purely sequential models are very capable at modeling syntactic phenomena, and using them result in very strong dependency parsers, for a variety of languages.