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arXiv · 2205.00274

Clues Before Answers: Generation-Enhanced Multiple-Choice QA

Zixian Huang·Ao Wu·Jiaying Zhou·Yu Gu·Yue Zhao·Gong Cheng
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Abstract

A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal. However, a side effect of twisting a generation target to fit the classification nature of MCQA is the under-utilization of the decoder and the knowledge that can be decoded. To exploit the generation capability and underlying knowledge of a pre-trained encoder-decoder model, in this paper, we propose a generation-enhanced MCQA model named GenMC. It generates a clue from the question and then leverages the clue to enhance a reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.

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Zixian Huang, Ao Wu, Jiaying Zhou, Yu Gu, Yue Zhao, Gong Cheng. 2022-04-30. Clues Before Answers: Generation-Enhanced Multiple-Choice QA. https://arxiv.org/abs/2205.00274

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