A Search-Based Dynamic Reranking Model for Dependency Parsing

Hao Zhou, Yue Zhang, Shujian Huang, Junsheng Zhou, Xin-Yu Dai, Jiajun Chen
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
We propose a novel reranking method to extend a deterministic neural dependency parser. Different to conventional k-best reranking, the proposed model integrates search and learning by utilizing a dynamic action revising process, using the reranking model to guide modification for the base outputs and to rerank the candidates. The dynamic reranking model achieves an absolute 1.78% accuracy improvement over the deterministic baseline parser on PTB, which is the highest improvement by neural rerankers in the literature.
doi:10.18653/v1/p16-1132 dblp:conf/acl/ZhouZHZDC16 fatcat:64mlokcsxfhyjhgbkytcha334q