让AI学会用自然语言解释推理过程，提升理解与泛化能力
Reasoning with Natural Language Explanations
- 基于自然语言解释构建推理模型，融合语义与逻辑结构
- 通过解释增强模型在下游任务中的泛化性能
- 适合对可解释性推理感兴趣的NLP研究者
解释是人类理性的重要特征，支撑学习、泛化以及科学发现与交流。随着自然语言推理（NLI）研究的发展，越来越多工作开始关注解释在学习与推理中的作用，致力于构建能够有效编码和利用自然语言解释的解释型NLI模型。解释性推理兼具实质与形式推理特点，为复杂推理建模提供了丰富场景。本文教程系统介绍了该领域的理论基础，涵盖解释的认知-语言学根基，梳理主流架构设计与评估方法，旨在帮助构建具备解释性推理能力的系统。
原文摘要 · Abstract (English)
Explanation constitutes an archetypal feature of human rationality, underpinning learning and generalisation, and representing one of the media supporting scientific discovery and communication. Due to the importance of explanations in human reasoning, an increasing amount of research in Natural Language Inference (NLI) has started reconsidering the role that explanations play in learning and inference, attempting to build explanation-based NLI models that can effectively encode and use natural language explanations on downstream tasks. Research in explanation-based NLI, however, presents specific challenges and opportunities, as explanatory reasoning reflects aspects of both material and formal inference, making it a particularly rich setting to model and deliver complex reasoning. In this tutorial, we provide a comprehensive introduction to the field of explanation-based NLI, grounding this discussion on the epistemological-linguistic foundations of explanations, systematically describing the main architectural trends and evaluation methodologies that can be used to build systems capable of explanatory reasoning.
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