Constituency Parsing On Penn Treebank

F1 score

평가 결과

이 벤치마크에서 각 모델의 성능 결과

비교 표

모델 이름	F1 score
improving-neural-parsing-by-disentangling	94.66
rethinking-self-attention-an-interpretable	96.38
recurrent-neural-network-grammars	93.3
tetra-tagging-word-synchronous-parsing-with	95.44
head-driven-phrase-structure-grammar-parsing	96.33
to-be-continuous-or-to-be-discrete-those-are	96.43
investigating-non-local-features-for-neural	95.92
what-do-recurrent-neural-network-grammars	93.6
fast-and-accurate-neural-crf-constituency-1	96.32
attention-is-all-you-need	92.7
constituency-parsing-with-a-self-attentive	95.13
improving-constituency-parsing-with-span	96.40
parsing-as-language-modeling	93.8
cloze-driven-pretraining-of-self-attention	95.6
n-ary-constituent-tree-parsing-with-recursive	95.92
head-driven-phrase-structure-grammar-parsing	95.84
syntactic-parse-fusion	92.6
direct-output-connection-for-a-high-rank	94.47
strongly-incremental-constituency-parsing	96.34
effective-self-training-for-parsing	92.1
to-be-continuous-or-to-be-discrete-those-are	96.03
grammar-as-a-foreign-language	92.1
in-order-transition-based-constituent-parsing	94.2
fast-and-accurate-neural-crf-constituency-1	95.69
generalizing-natural-language-analysis-1	95.5
fast-and-accurate-neural-crf-constituency-1	94.12
an-empirical-study-of-building-a-strong	94.32