Stack-based Multi-layer Attention for Transition-based Dependency Parsing

Zhirui Zhang, Shujie Liu, Mu Li, Ming Zhou, Enhong Chen


Abstract
Although sequence-to-sequence (seq2seq) network has achieved significant success in many NLP tasks such as machine translation and text summarization, simply applying this approach to transition-based dependency parsing cannot yield a comparable performance gain as in other state-of-the-art methods, such as stack-LSTM and head selection. In this paper, we propose a stack-based multi-layer attention model for seq2seq learning to better leverage structural linguistics information. In our method, two binary vectors are used to track the decoding stack in transition-based parsing, and multi-layer attention is introduced to capture multiple word dependencies in partial trees. We conduct experiments on PTB and CTB datasets, and the results show that our proposed model achieves state-of-the-art accuracy and significant improvement in labeled precision with respect to the baseline seq2seq model.
Anthology ID:
D17-1175
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1677–1682
Language:
URL:
https://www.aclweb.org/anthology/D17-1175
DOI:
10.18653/v1/D17-1175
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1175.pdf