Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations

2016-03-14 · TACL 2016 1 · Eliyahu Kiperwasser, Yoav Goldberg

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.

📄 PDF Abstract BibTeX arXiv:1603.04351

Code (1)

elikip/bist-parser 공식 구현 pytorch

Tasks

Dependency ParsingSentence

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