Encoder-Decoder Shift-Reduce Syntactic Parsing

Starting from NMT, encoder-decoder neu- ral networks have been used for many NLP problems. Graph-based models and transition-based models borrowing the en- coder components achieve state-of-the-art performance on dependency parsing and constituent parsing, respectively. How- ever, there has not been...

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Bibliographic Details
Published in:arXiv.org 2017-06
Main Authors: Liu, Jiangming, Zhang, Yue
Format: Article
Language:English
Subjects:
Dependence
Encoders-Decoders
Transportation networks
Online Access:Get full text
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Summary:Starting from NMT, encoder-decoder neu- ral networks have been used for many NLP problems. Graph-based models and transition-based models borrowing the en- coder components achieve state-of-the-art performance on dependency parsing and constituent parsing, respectively. How- ever, there has not been work empirically studying the encoder-decoder neural net- works for transition-based parsing. We apply a simple encoder-decoder to this end, achieving comparable results to the parser of Dyer et al. (2015) on standard de- pendency parsing, and outperforming the parser of Vinyals et al. (2015) on con- stituent parsing.
ISSN:2331-8422