TTTTTackling WinoGrande Schemas
Sheng-Chieh Lin, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai,, Chuan-Ju Wang, Jimmy Lin
TL;DR
This paper presents a novel approach using the T5 sequence-to-sequence model to improve performance on the WinoGrande challenge, achieving the best known results at the time.
Contribution
The authors applied T5 with a unique decomposition method to significantly outperform previous state-of-the-art results on WinoGrande.
Findings
Achieved 0.7673 AUC on WinoGrande
Outperformed previous state-of-the-art by over five points
Demonstrated effectiveness of T5 in reasoning tasks
Abstract
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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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
MethodsLinear Layer · Gated Linear Unit · Refunds@Expedia|||How do I get a full refund from Expedia? · Byte Pair Encoding · Multi-Head Attention · Adafactor · Residual Connection · Inverse Square Root Schedule · Attention Dropout · SentencePiece