Conference article

Capturing Dependency Syntax with "Deep" Sequential Models

Yoav Goldberg
Bar Ilan University, Department of Computer Science, Ramat-Gan, Israel

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Published in: Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017), September 18-20, 2017, Università di Pisa, Italy

Linköping Electronic Conference Proceedings 139:1, s. 1

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Published: 2017-09-13

ISBN: 978-91-7685-467-9

ISSN: 1650-3686 (print), 1650-3740 (online)


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. In this talk, I will briefly describe recurrent-networks, and present empirical evidence for their capabilities of learning the subject-verb agreement relation in naturally occuring text, from relatively indirect supervision. This part is based on my joint work with Tal Linzen and Emmanuel Dupoux. I will then describe bi-directional recurrent networks---a simple extension of recurrent networks---and show how they can be used as the basis of state-of-the-art dependency parsers. This is based on my work with Eliyahu Kipperwasser, but will also touch on work by other researchers in that space.


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