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Rethinking Label Smoothing on Multi-hop Question Answering

2022-12-19 14:48:08
Zhangyue Yin, Yuxin Wang, Yiguang Wu, Hang Yan, Xiannian Hu, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Xipeng Qiu
arXiv_CL
             
Abstract

Label smoothing is a regularization technique widely used in supervised learning to improve the generalization of models on various tasks, such as image classification and machine translation. However, the effectiveness of label smoothing in multi-hop question answering (MHQA) has yet to be well studied. In this paper, we systematically analyze the role of label smoothing on various modules of MHQA and propose F1 smoothing, a novel label smoothing technique specifically designed for machine reading comprehension (MRC) tasks. We evaluate our method on the HotpotQA dataset and demonstrate its superiority over several strong baselines, including models that utilize complex attention mechanisms. Our results suggest that label smoothing can be effective in MHQA, but the choice of smoothing strategy can significantly affect performance.

Abstract (translated)

URL

https://arxiv.org/abs/2212.09512

PDF

https://arxiv.org/pdf/2212.09512.pdf

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