Efficient Second-Order TreeCRF for Neural Dependency Parsing

ACL 2020 Yu ZhangZhenghua LiMin Zhang

In the deep learning (DL) era, parsing models are extremely simplified with little hurt on performance, thanks to the remarkable capability of multi-layer BiLSTMs in context representation. As the most popular graph-based dependency parser due to its high efficiency and performance, the biaffine parser directly scores single dependencies under the arc-factorization assumption, and adopts a very simple local token-wise cross-entropy training loss... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Dependency Parsing CoNLL-2009 CRFPar LAS 86.52 # 1
UAS 89.63 # 1
Chinese Dependency Parsing CoNLL-2009 CRFPar LAS 86.52 # 1
UAS 89.63 # 1
Dependency Parsing NLPCC-2019 CRFPar LAS 72.33 # 1
Dependency Parsing Penn Treebank CRFPar UAS 96.14 # 3
LAS 94.49 # 4

Methods used in the Paper


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