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.

本论文系统分析了标签平滑法在多跳问题回答中的作用，并提出了一种专门为机器阅读理解任务设计的新型标签平滑技术 F1 smoothing，在 HotpotQA 数据集上取得了比复杂注意力机制模型等强基线模型更好的效果，结果表明标签平滑法在多跳问题回答中有效，但平滑策略的选择会对性能产生显著影响。