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Models/Sys1-Primary

Sys1-Primary

Reported on 10 benchmarks across 2 tasks

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

Adversarial5 results

  • Text GenerationonE2E NLG Challenge
    BLEU· uses extra data
    65.61
    best: 68.6 (S_1^R)
  • Text GenerationonE2E NLG Challenge
    CIDEr· uses extra data
    2.2183
    best: 2.37 (S_1^R)
  • Text GenerationonE2E NLG Challenge
    METEOR· uses extra data
    45.17
    best: 46.11 (Self-memory)
  • Text GenerationonE2E NLG Challenge
    NIST· uses extra data
    8.5105
    best: 8.73 (S_1^R)
  • Text GenerationonE2E NLG Challenge
    ROUGE-L· uses extra data
    68.39
    best: 70.83 (Zhang)

Natural Language Processing5 results

  • Data-to-Text GenerationonE2E NLG Challenge
    BLEU· uses extra data
    65.61
    best: 68.6 (S_1^R)
  • Data-to-Text GenerationonE2E NLG Challenge
    CIDEr· uses extra data
    2.2183
    best: 2.37 (S_1^R)
  • Data-to-Text GenerationonE2E NLG Challenge
    METEOR· uses extra data
    45.17
    best: 46.11 (Self-memory)
  • Data-to-Text GenerationonE2E NLG Challenge
    NIST· uses extra data
    8.5105
    best: 8.73 (S_1^R)
  • Data-to-Text GenerationonE2E NLG Challenge
    ROUGE-L· uses extra data
    68.39
    best: 70.83 (Zhang)