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Investigating Non-local Features for Neural Constituency Parsing

2021-09-27 06:14:30
Leyang Cui, Sen Yang, Yue Zhang

Abstract

Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the training process of a local span-based parser, by predicting constituent n-gram non-local patterns and ensuring consistency between non-local patterns and local constituents. Results show that our simple method gives better results than the CRF parser on both PTB and CTB. Besides, our method achieves state-of-the-art BERT-based performance on PTB (95.92 F1) and strong performance on CTB (92.31 F1).

Abstract (translated)

URL

https://arxiv.org/abs/2109.12814

PDF

https://arxiv.org/pdf/2109.12814.pdf


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