Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

ACL 2018

Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

Jan 28, 2021
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Abstract: In this work, we propose a novel constituency parsing scheme. The model first predicts a real-valued scalar, named syntactic distance, for each split position in the sentence. The topology of grammar tree is then determined by the values of syntactic distances. Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. It is also easier to parallelize and much faster. Our model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, which surpasses the previous single model results by a large margin. Authors: Yikang Shen, Zhouhan Lin, Athul Paul Jacob, Alessandro Sordoni, Aaron Courville, Yoshua Bengio (University of Montréal, University of Waterloo, Microsoft Research)

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