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Models/Ours: cross-sentence

Ours: cross-sentence

Reported on 7 benchmarks across 3 tasks · 1 paper · 2 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing4 results

  • Relation ExtractiononSciERC
    RE+ Micro F1· 2020-10-24
    36.7
    best: 41.6 (PL-Marker)
    SOTA
    A Frustratingly Easy Approach for Entity and Relation ExtractionarXiv:2010.12812
  • Relation ExtractiononSciERC
    Entity F1· 2020-10-24
    68.9
    best: 70.53 (SpERT.PL (SciBERT))
    A Frustratingly Easy Approach for Entity and Relation ExtractionarXiv:2010.12812
  • Relation ExtractiononSciERC
    Relation F1· 2020-10-24
    50.1
    best: 53.2 (PL-Marker)
    A Frustratingly Easy Approach for Entity and Relation ExtractionarXiv:2010.12812
  • Named Entity Recognition (NER)onSciERC
    F1· 2020-10-24
    68.2
    best: 72.4 (SciDeBERTa v2)
    A Frustratingly Easy Approach for Entity and Relation ExtractionarXiv:2010.12812

Medical3 results

  • Information ExtractiononSciERC
    RE+ Micro F1· 2020-10-24
    36.7
    best: 41.6 (PL-Marker)
    SOTA
    A Frustratingly Easy Approach for Entity and Relation ExtractionarXiv:2010.12812
  • Information ExtractiononSciERC
    Entity F1· 2020-10-24
    68.9
    best: 70.53 (SpERT.PL (SciBERT))
    A Frustratingly Easy Approach for Entity and Relation ExtractionarXiv:2010.12812
  • Information ExtractiononSciERC
    Relation F1· 2020-10-24
    50.1
    best: 53.2 (PL-Marker)
    A Frustratingly Easy Approach for Entity and Relation ExtractionarXiv:2010.12812