HyperAI超神经

Amr Parsing On Ldc2017T10

评估指标

Smatch

评测结果

各个模型在此基准测试上的表现结果

比较表格
模型名称Smatch
oxford-at-semeval-2017-task-9-neural-amr61.9
maximum-bayes-smatch-ensemble-distillation85.9
one-spring-to-rule-them-both-symmetric-amr84.3
core-semantic-first-a-top-down-approach-for73.2
transition-based-parsing-with-stack79.0
incorporating-graph-information-in86.1
atp-amrize-then-parse-enhancing-amr-parsing85.2
graph-pre-training-for-amr-parsing-and-185.4
improving-amr-parsing-with-sequence-to81.4
amr-parsing-via-graph-sequence-iterative80.2
amr-parsing-as-sequence-to-graph-transduction76.3
broad-coverage-semantic-parsing-as77.0
incorporating-graph-information-in84.7
bibl-amr-parsing-and-generation-with84.6
amr-parsing-with-action-pointer-transformer82.6
a-differentiable-relaxation-of-graph76.1
ensembling-graph-predictions-for-amr-parsing86.26
structure-aware-fine-tuning-of-sequence-to84.7
levi-graph-amr-parser-using-heterogeneous80
bibl-amr-parsing-and-generation-with84.7
atp-amrize-then-parse-enhancing-amr-parsing85.3
rewarding-smatch-transition-based-amr-parsing73.4
maximum-bayes-smatch-ensemble-distillation86.7
neural-semantic-parsing-by-character-based71.0
ensembling-graph-predictions-for-amr-parsing85.85
amr-parsing-as-graph-prediction-with-latent74.4
pushing-the-limits-of-amr-parsing-with-self81.3