利用词级信息提升文本分类效果，实证验证双向增强机制
Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt
- 用词级与文本级分类相互促进的思路改进少样本分类
- 18/21数据集上F1分数超越基线，证明该机制有效
- 适合研究提示学习与少样本分类的学者参考
互增强效应（MRE）探讨词级与文本级分类之间的协同关系，认为二者性能可相互提升。然而先前研究尚未充分验证或解释此机制。为此，我们通过实证实验观察并证实了MRE的存在。在21个MRE Mix数据集上的对比实验表明，微调后模型表现出显著的互增强现象。进一步地，我们将MRE应用于提示学习，利用词级信息作为提示词（verbalizer）来增强模型对文本级标签的预测能力。最终实验中，18个数据集的F1分数显著超过基线，验证了词级信息有助于提升语言模型对整体文本的理解。
原文摘要 · Abstract (English)
The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks. It posits that the performance of both classification levels can be mutually enhanced. However, this mechanism has not been adequately demonstrated or explained in prior research. To address this gap, we employ empirical experiment to observe and substantiate the MRE theory. Our experiments on 21 MRE mix datasets revealed the presence of MRE in the model and its impact. Specifically, we conducted compare experiments use fine-tune. The results of findings from comparison experiments corroborates the existence of MRE. Furthermore, we extended the application of MRE to prompt learning, utilizing word-level information as a verbalizer to bolster the model's prediction of text-level classification labels. In our final experiment, the F1-score significantly surpassed the baseline in 18 out of 21 MRE Mix datasets, further validating the notion that word-level information enhances the language model's comprehension of the text as a whole.
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