methods

  • stacking
  • multi-view learning
  • pretraining

transition-based method: IMAGE

根据状态灵活提取特征,不用针对整个图提取特征

features:

uni-/bi-gram embedding + length-5-window

结构学习(而不是动作学习)更有用

beam search: 保留 gold action sequence 而不是优化最高得分

NMT:encoder 的作用较大,并不是一个结构 search 的难题

methods

  • distillation
  • multitask
    • adversarial

ZPar: http://people.sutd.edu.sg/~yue_zhang/doc/index.html

A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing http://anthology.aclweb.org/P/P15/P15-1117.pdf 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 1.8% accuracy improvement upon a competitive greedy neural parser baseline, giving performance comparable to the best linear parser.

Syntactic Processing Using the Generalized Perceptron and Beam Search http://www.mitpressjournals.org/doi/pdfplus/10.1162/coli_a_00037 http://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00037

Neural Word Segmentation with Rich Pretraining https://arxiv.org/pdf/1704.08960.pdf https://arxiv.org/abs/1704.08960

Neural Network for Heterogeneous Annotations http://www.aclweb.org/old_anthology/D/D16/D16-1070.pdf