Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiL...

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Bibliographic Details
Published in:Transactions of the Association for Computational Linguistics 2016-12, Vol.4, p.313-327
Main Authors: Kiperwasser, Eliyahu, Goldberg, Yoav
Format: Article
Language:English
Subjects:
Chinese languages
English language
Feature extraction
Parsers
Parsing
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Summary:We present a simple and effective scheme for dependency parsing which is based on bidirectional-LSTMs (BiLSTMs). Each sentence token is associated with a BiLSTM vector representing the token in its sentential context, and feature vectors are constructed by concatenating a few BiLSTM vectors. The BiLSTM is trained jointly with the parser objective, resulting in very effective feature extractors for parsing. We demonstrate the effectiveness of the approach by applying it to a greedy transition-based parser as well as to a globally optimized graph-based parser. The resulting parsers have very simple architectures, and match or surpass the state-of-the-art accuracies on English and Chinese.
ISSN:2307-387X
2307-387X
DOI:10.1162/tacl_a_00101