Abstract
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
Abstract (translated)
隐式话语关系识别（IDRR）仍然是一项具有挑战性的任务，因为它要求在缺乏明确的话语标记的情况下进行深度语义理解。现有方法的另一个限制是它们仅预测关系而不提供任何支持性解释。近年来，大型语言模型（LLMs）在深入的语言理解和自然语言解释生成方面表现出强大的推理能力。在这项工作中，我们提出了一种简单而有效的方法，即将LLM的推理能力提炼到轻量级IDRR模型中，以提高性能和可解释性。具体而言，我们首先提示一个LLM根据每个训练实例的黄金标签生成解释。然后，我们引入了一个新颖的分类-生成框架，该框架同时进行关系预测和解释生成，并通过附加的LLM生成的解释监督对其进行训练。我们的框架是即插即用型，能够轻松集成到大多数现有的IDRR模型中。 在PDTB数据集上的实验结果表明，我们的方法显著提高了IDRR性能，而人类评估进一步证实了所生成的解释增强了模型的可解释性。此外，我们还验证了该方法在情感分类和自然语言推理任务中的通用性。
URL
https://arxiv.org/abs/2602.21763
