Papers › TTTTTackling WinoGrande Schemas

TTTTTackling WinoGrande Schemas

18 Mar 2020arXiv:2003.08380archive 2025-07-28

Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, Jimmy Lin

We applied the T5 sequence-to-sequence model to tackle the AI2 WinoGrande Challenge by decomposing each example into two input text strings, each containing a hypothesis, and using the probabilities assigned to the "entailment" token as a score of the hypothesis. Our first (and only) submission to the official leaderboard yielded 0.7673 AUC on March 13, 2020, which is the best known result at this time and beats the previous state of the art by over five points.

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Tasks

Coreference Resolution Winogrande

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Coreference Resolution	Winograd Schema Challenge	TTTTT 3B (fine-tuned)	Accuracy	84.6	#17 of 82	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adafactor Attention Attention Dropout BPE Dense Connections Dropout Gated Linear Unit Inverse Square Root Schedule Layer Normalization Linear Layer Multi-Head Attention Residual Connection SentencePiece Softmax T5

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