Investigating Non-local Features for Neural Constituency Parsing
Leyang Cui, Sen Yang, Yue Zhang · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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 self-attentive 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).Our parser also achieves better or competitive performance in multilingual and zero-shot cross-domain settings compared with the baseline.