Syntax Aware LSTM model for Semantic Role Labeling

Feng Qian, Lei Sha, Baobao Chang, Lu-chen Liu, Ming Zhang


Abstract
In Semantic Role Labeling (SRL) task, the tree structured dependency relation is rich in syntax information, but it is not well handled by existing models. In this paper, we propose Syntax Aware Long Short Time Memory (SA-LSTM). The structure of SA-LSTM changes according to dependency structure of each sentence, so that SA-LSTM can model the whole tree structure of dependency relation in an architecture engineering way. Experiments demonstrate that on Chinese Proposition Bank (CPB) 1.0, SA-LSTM improves F1 by 2.06% than ordinary bi-LSTM with feature engineered dependency relation information, and gives state-of-the-art F1 of 79.92%. On English CoNLL 2005 dataset, SA-LSTM brings improvement (2.1%) to bi-LSTM model and also brings slight improvement (0.3%) when added to the state-of-the-art model.
Anthology ID:
W17-4305
Volume:
Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–32
Language:
URL:
https://www.aclweb.org/anthology/W17-4305
DOI:
10.18653/v1/W17-4305
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4305.pdf