Syntax Aware LSTM model for Semantic Role Labeling

Feng Qian, Lei Sha, Baobao Chang, LuChen Liu, Ming Zhang
<span title="">2017</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/u3ideoxy4fghvbsstiknuweth4" style="color: black;">Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing</a> </i> &nbsp;
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
more &raquo; ... improves F 1 by 2.06% than ordinary bi-LSTM with feature engineered dependency relation information, and gives state-of-the-art F 1 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 stateof-the-art model.
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