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Models/Robustly Fine-tuned Graph-based Recurrent Retriever

Robustly Fine-tuned Graph-based Recurrent Retriever

Reported on 6 benchmarks across 1 task · 1 paper · 3 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 AnsweringonHotpotQA
    ANS-EM· 2019-11-24
    0.6
    best: 0.727 (Beam Retrieval)
    SOTA
    Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question AnsweringarXiv:1911.10470
  • Question AnsweringonHotpotQA
    ANS-F1· 2019-11-24
    0.73
    best: 0.85 (Beam Retrieval)
    SOTA
    Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question AnsweringarXiv:1911.10470
  • Question AnsweringonHotpotQA
    JOINT-F1· 2019-11-24
    0.612
    best: 0.775 (Beam Retrieval)
    SOTA
    Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question AnsweringarXiv:1911.10470
  • Question AnsweringonHotpotQA
    JOINT-EM· 2019-11-24
    0.354
    best: 0.505 (Beam Retrieval)
    Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question AnsweringarXiv:1911.10470
  • Question AnsweringonHotpotQA
    SUP-EM· 2019-11-24
    0.491
    best: 0.663 (Beam Retrieval)
    Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question AnsweringarXiv:1911.10470
  • Question AnsweringonHotpotQA
    SUP-F1· 2019-11-24
    0.764
    best: 0.901 (Beam Retrieval)
    Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question AnsweringarXiv:1911.10470