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End-to-end Semantic Role Labeling with Neural Transition-based Model

AAAI 2021 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining · 28 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add incremental high-order argument-predicate scoring features (ablation)Transition SRL · UPB Avg.F1: 72.9 -> 74.2Helped
Add dependency-tree features (ablation)Transition SRL · CoNLL09 development setArg. F1: 89.8 -> 91.0Helped
Add POS features (ablation)Transition SRL · CoNLL09 development setArg. F1: 90.4 -> 91.0Helped
Add character encoder (ablation)Transition SRL · CoNLL09 development setArg. F1: 90.7 -> 91.0Helped
Add high-order action feature o^a (ablation)Transition SRL · CoNLL09 development setArg. F1: 89.4 -> 91.0Helped
Add high-order role feature o^r (ablation)Transition SRL · CoNLL09 development setArg. F1: 88.8 -> 91.0Helped
Use close-first parsing instead of left-to-right parsing (ablation)Transition SRL · CoNLL09 development setArg. F1: 88.5 -> 91.0Helped
Use close-first parsing instead of right-to-left parsing (ablation)Transition SRL · CoNLL09 development setArg. F1: 88.0 -> 91.0Helped
Use BERT contextualized word representationsHigh-order transition SRL · CoNLL09 with contextualized representationsF1Helped
Use XLNet contextualized word representationsHigh-order transition SRL · CoNLL09 with contextualized representationsF1Helped
Use incremental high-order interaction rather than the vanilla transition model (ablation)Transition SRL · CoNLL09 development setArg. F1: 88.6 -> 91.0Helped

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