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

8 years 5 days ago

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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

Hao Zhou, Yue Zhang, Shujian Huang, Jiajun Chen

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