A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing

Neural probabilistic parsers are attractive for their capability of automatic feature combination and small data sizes. A transition-based greedy neural parser has given better accuracies over its linear counterpart. We propose a neural probabilistic structured-prediction model for transition-based dependency parsing, which integrates search and learning. Beam search is used for decoding, and contrastive learning is performed for maximizing the sentence-level log-likelihood. In standard Penn Treebank experiments, the structured neural parser achieves a

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Hao Zhou, Yue Zhang, Shujian Huang, Jiajun Chen
• Keywords: ACL 2015, Computational Linguistics