@inproceedings{zhangyue-etal-2023-rethinking,
title = "Rethinking Label Smoothing on Multi-hop Question Answering",
author = "Zhangyue, Yin and
Yuxin, Wang and
Xiannian, Hu and
Yiguang, Wu and
Hang, Yan and
Xinyu, Zhang and
Zhao, Cao and
Xuanjing, Huang and
Xipeng, Qiu",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-1.53",
pages = "611--623",
abstract = "{``}Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiringmultiple reasoning components, including document retrieval, supporting sentence prediction,and answer span extraction. In this work, we present the first application of label smoothing tothe MHQA task, aiming to enhance generalization capabilities in MHQA systems while miti-gating overfitting of answer spans and reasoning paths in the training set. We introduce a novellabel smoothing technique, F1 Smoothing, which incorporates uncertainty into the learning pro-cess and is specifically tailored for Machine Reading Comprehension (MRC) tasks. Moreover,we employ a Linear Decay Label Smoothing Algorithm (LDLA) in conjunction with curricu-lum learning to progressively reduce uncertainty throughout the training process. Experimenton the HotpotQA dataset confirms the effectiveness of our approach in improving generaliza-tion and achieving significant improvements, leading to new state-of-the-art performance on theHotpotQA leaderboard.{''}",
language = "English",
}
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<abstract>“Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiringmultiple reasoning components, including document retrieval, supporting sentence prediction,and answer span extraction. In this work, we present the first application of label smoothing tothe MHQA task, aiming to enhance generalization capabilities in MHQA systems while miti-gating overfitting of answer spans and reasoning paths in the training set. We introduce a novellabel smoothing technique, F1 Smoothing, which incorporates uncertainty into the learning pro-cess and is specifically tailored for Machine Reading Comprehension (MRC) tasks. Moreover,we employ a Linear Decay Label Smoothing Algorithm (LDLA) in conjunction with curricu-lum learning to progressively reduce uncertainty throughout the training process. Experimenton the HotpotQA dataset confirms the effectiveness of our approach in improving generaliza-tion and achieving significant improvements, leading to new state-of-the-art performance on theHotpotQA leaderboard.”</abstract>
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%0 Conference Proceedings
%T Rethinking Label Smoothing on Multi-hop Question Answering
%A Zhangyue, Yin
%A Yuxin, Wang
%A Xiannian, Hu
%A Yiguang, Wu
%A Hang, Yan
%A Xinyu, Zhang
%A Zhao, Cao
%A Xuanjing, Huang
%A Xipeng, Qiu
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F zhangyue-etal-2023-rethinking
%X “Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiringmultiple reasoning components, including document retrieval, supporting sentence prediction,and answer span extraction. In this work, we present the first application of label smoothing tothe MHQA task, aiming to enhance generalization capabilities in MHQA systems while miti-gating overfitting of answer spans and reasoning paths in the training set. We introduce a novellabel smoothing technique, F1 Smoothing, which incorporates uncertainty into the learning pro-cess and is specifically tailored for Machine Reading Comprehension (MRC) tasks. Moreover,we employ a Linear Decay Label Smoothing Algorithm (LDLA) in conjunction with curricu-lum learning to progressively reduce uncertainty throughout the training process. Experimenton the HotpotQA dataset confirms the effectiveness of our approach in improving generaliza-tion and achieving significant improvements, leading to new state-of-the-art performance on theHotpotQA leaderboard.”
%U https://aclanthology.org/2023.ccl-1.53
%P 611-623
Markdown (Informal)
[Rethinking Label Smoothing on Multi-hop Question Answering](https://aclanthology.org/2023.ccl-1.53) (Zhangyue et al., CCL 2023)
ACL
- Yin Zhangyue, Wang Yuxin, Hu Xiannian, Wu Yiguang, Yan Hang, Zhang Xinyu, Cao Zhao, Huang Xuanjing, and Qiu Xipeng. 2023. Rethinking Label Smoothing on Multi-hop Question Answering. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 611–623, Harbin, China. Chinese Information Processing Society of China.