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Models/End-to-end Top-down (XLNet)

End-to-end Top-down (XLNet)

Reported on 7 benchmarks across 1 task · 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 Processing7 results

  • Discourse ParsingonRST-DT
    RST-Parseval (Nuclearity)· 2021-05-23
    76
    SOTA
    RST Parsing from ScratcharXiv:2105.10861
  • Discourse ParsingonRST-DT
    RST-Parseval (Relation)· 2021-05-23
    61.8
    best: 63.2 (Top-down Span-based Parser with Silver Agreement Subtrees (ensemble))
    SOTA
    RST Parsing from ScratcharXiv:2105.10861
  • Discourse ParsingonRST-DT
    RST-Parseval (Span)· 2021-05-23
    87.6
    SOTA
    RST Parsing from ScratcharXiv:2105.10861
  • Discourse ParsingonRST-DT
    Standard Parseval (Nuclearity)· 2021-05-23
    64.3
    best: 70.4 (Bottom-up Llama 2 (70B))
    SOTA
    RST Parsing from ScratcharXiv:2105.10861
  • Discourse ParsingonRST-DT
    Standard Parseval (Relation)· 2021-05-23
    51.6
    best: 60 (Bottom-up Llama 2 (70B))
    SOTA
    RST Parsing from ScratcharXiv:2105.10861
  • Discourse ParsingonRST-DT
    Standard Parseval (Span)· 2021-05-23
    74.3
    best: 79.8 (Bottom-up Llama 2 (70B))
    SOTA
    RST Parsing from ScratcharXiv:2105.10861
  • Discourse ParsingonRST-DT
    Standard Parseval (Full)· 2021-05-23
    50.2
    best: 58.1 (Bottom-up Llama 2 (70B))
    RST Parsing from ScratcharXiv:2105.10861