Deep Biaffine Attention for Neural Dependency Parsing

This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels... (read more)

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Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Dependency Parsing CoNLL-2009 Biaffine Parser LAS 85.38 # 2
UAS 88.90 # 2
Dependency Parsing Penn Treebank Deep Biaffine + RoBERTa UAS 97.29 # 2
LAS 95.83 # 2
Dependency Parsing Penn Treebank Deep Biaffine POS 97.3 # 4
UAS 95.44 # 12
LAS 93.76 # 13

Methods used in the Paper


METHOD TYPE
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