用大模型生成解释，让隐式语义关系识别更准更可懂
Improving Implicit Discourse Relation Recognition with Natural Language Explanations from LLMs
- 用大模型为训练样本生成自然语言解释，作为额外监督信号
- 在PDTB数据集上准确率显著提升，解释生成质量获人工验证
- 方法轻量易插拔，适用于多种文本任务
隐式语义关系识别（IDRR）因缺乏显式连接词，需深层语义理解而极具挑战。现有方法仅输出关系类别，无法提供解释。近期大语言模型（LLMs）在深度语言理解和自然语言解释生成方面表现突出。本文提出一种简单有效的方案：先用大模型基于真实标签为每个训练样本生成解释，再设计一个联合分类与生成的框架，利用这些解释进行联合训练。该框架可无缝集成至多数现有IDRR模型中。在PDTB数据集上的实验表明，该方法显著提升识别性能，人工评估也证实生成解释有效增强了模型可解释性。此外，方法在情感分类和自然语言推理任务上也展现出良好泛化能力。
原文摘要 · Abstract (English)
Implicit Discourse Relation Recognition (IDRR) remains a challenging task due to the requirement for deep semantic understanding in the absence of explicit discourse markers. A further limitation is that existing methods only predict relations without providing any supporting explanations. Recent advances in large language models (LLMs) have shown strong reasoning capabilities in both deep language understanding and natural language explanation generation. In this work, we propose a simple yet effective approach to distill the reasoning capabilities of LLMs into lightweight IDRR models to improve both performance and interpretability. Specifically, we first prompt an LLM to generate explanations for each training instance conditioned on its gold label. Then, we introduce a novel classification-generation framework that jointly performs relation prediction and explanation generation, and train it with the additional supervision of LLM-generated explanations. Our framework is plug-and-play, enabling easy integration with most existing IDRR models. Experimental results on PDTB demonstrate that our approach significantly improves IDRR performance, while human evaluation further confirms that the generated explanations enhance model interpretability. Furthermore, we validate the generality of our approach on sentiment classification and natural language inference
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