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Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction

AAAI 2021 · Architectures and efficiency › Attention and transformers · approach: Long-context KV and memory attention · 192 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add structured self-attention with biaffine transformationRoBERTa Large on DocRED · DocRED testF1: 60.54 -> 61.42Helped
Pretrain SSAN on distantly supervised data before annotated-data finetuningSSAN Biaffine (RoBERTa Large) · DocRED testF1: 65.92Helped
Add structured self-attention with biaffine transformationSciBERT on CDR · CDR testF1: 65.8 -> 68.7Helped
Add structured self-attention with biaffine transformationBERT Large on GDA · GDA testF1: 81.6 -> 83.9Helped
Add intra+coref dependency (ablation)SSAN Biaffine (RoBERTa Large) · DocRED devF1: 61.57 -> 62.08Helped
Add intra+relate dependency (ablation)SSAN Biaffine (RoBERTa Large) · DocRED devF1: 61.91 -> 62.08Helped
Add inter+coref dependency (ablation)SSAN Biaffine (RoBERTa Large) · DocRED devF1: 61.74 -> 62.08Helped
Add inter+relate dependency (ablation)SSAN Biaffine (RoBERTa Large) · DocRED devF1: 61.84 -> 62.08Helped
Add intraNE dependency (ablation)SSAN Biaffine (RoBERTa Large) · DocRED devF1: 61.97 -> 62.08Helped
Add all five entity dependencies (ablation)RoBERTa Large baseline · DocRED devF1: 60.58 -> 62.08Helped
Add dependency-specific prior bias (ablation)RoBERTa Large baseline · DocRED devF1: 60.58 -> 60.59No clear effect
Add query-conditioned bias (ablation)RoBERTa Large baseline · DocRED devF1: 60.58 -> 61.31Helped

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