  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.
