@inproceedings{zhangyue-etal-2023-rethinking,
    title = "Rethinking Label Smoothing on Multi-hop Question Answering",
    author = "Yin, Zhangyue  and
      Wang, Yuxin  and
      Hu, Xiannian  and
      Wu, Yiguang  and
      Yan, Hang  and
      Zhang, Xinyu  and
      Cao, Zhao  and
      Huang, Xuanjing  and
      Qiu, Xipeng",
    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://preview.aclanthology.org/ingest-jclib/2023.ccl-1.53/",
    pages = "611--623",
    language = "eng",
    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.''"
}