Bi-directional Attention with Agreement for Dependency Parsing

We develop a novel bi-directional attention model for dependency parsing, which learns to agree on headword predictions from the forward and backward parsing directions. The parsing procedure for each direction is formulated as sequentially querying the memory component that stores continuous headword embeddings. The proposed parser makes use of {\it soft} headword embeddings, allowing the model to implicitly capture high-order parsing history without dramatically increasing the computational complexity. We conduct experiments on English, Chinese, and 12 other languages from the CoNLL 2006 shared task, showing that the proposed model achieves state-of-the-art unlabeled attachment scores on 6 languages.

PDF Abstract EMNLP 2016 PDF EMNLP 2016 Abstract

Datasets


  Add Datasets introduced or used in this paper
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Chinese Dependency Parsing Chinese Pennbank Cheng et al. (2016) LAS 85.7 # 4
UAS 88.1 # 2

Methods


No methods listed for this paper. Add relevant methods here