A Search-Based Dynamic Reranking Model for Dependency Parsing
Hao Zhou, Yue Zhang, Shujian Huang, Junsheng Zhou, Xinyu Dai, Jiajun Chen · 2016
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.