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Models/E-MCA

E-MCA

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

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

Natural Language Processing6 results

  • Question AnsweringonELI5
    Rouge-1· 2019-10-18
    30
    best: 30.6 (BART)
    SOTA
    Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsarXiv:1910.08435
  • Question AnsweringonELI5
    Rouge-2· 2019-10-18
    5.8
    best: 10.36 (QG)
    SOTA
    Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsarXiv:1910.08435
  • Question AnsweringonELI5
    Rouge-L· 2019-10-18
    24
    best: 26.9 (Fourier Transformer)
    SOTA
    Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsarXiv:1910.08435
  • Open-Domain Question AnsweringonELI5
    Rouge-1· 2019-10-18
    30
    best: 30.6 (BART)
    SOTA
    Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsarXiv:1910.08435
  • Open-Domain Question AnsweringonELI5
    Rouge-2· 2019-10-18
    5.8
    best: 10.36 (QG)
    SOTA
    Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsarXiv:1910.08435
  • Open-Domain Question AnsweringonELI5
    Rouge-L· 2019-10-18
    24
    best: 26.9 (Fourier Transformer)
    SOTA
    Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document InputsarXiv:1910.08435