@inproceedings{huang-etal-2022-clues,
title = "Clues Before Answers: Generation-Enhanced Multiple-Choice {QA}",
author = "Huang, Zixian and
Wu, Ao and
Zhou, Jiaying and
Gu, Yu and
Zhao, Yue and
Cheng, Gong",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.239",
doi = "10.18653/v1/2022.naacl-main.239",
pages = "3272--3287",
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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<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.</abstract>
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%0 Conference Proceedings
%T Clues Before Answers: Generation-Enhanced Multiple-Choice QA
%A Huang, Zixian
%A Wu, Ao
%A Zhou, Jiaying
%A Gu, Yu
%A Zhao, Yue
%A Cheng, Gong
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F huang-etal-2022-clues
%X 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.
%R 10.18653/v1/2022.naacl-main.239
%U https://aclanthology.org/2022.naacl-main.239
%U https://doi.org/10.18653/v1/2022.naacl-main.239
%P 3272-3287
Markdown (Informal)
[Clues Before Answers: Generation-Enhanced Multiple-Choice QA](https://aclanthology.org/2022.naacl-main.239) (Huang et al., NAACL 2022)
ACL
- Zixian Huang, Ao Wu, Jiaying Zhou, Yu Gu, Yue Zhao, and Gong Cheng. 2022. Clues Before Answers: Generation-Enhanced Multiple-Choice QA. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3272–3287, Seattle, United States. Association for Computational Linguistics.