This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct definition for natural language reasoning in NLP, based on both philosophy and NLP scenarios, discuss what types of tasks require reasoning, and introduce a taxonomy of reasoning. Practically, we conduct a comprehensive literature review on natural language reasoning in NLP, mainly covering classical logical reasoning, natural language inference, multi-hop question answering, and commonsense reasoning. The paper also identifies and views backward reasoning, a powerful paradigm for multi-step reasoning, and introduces defeasible reasoning as one of the most important future directions in natural language reasoning research. We focus on single-modality unstructured natural language text, excluding neuro-symbolic research and mathematical reasoning.

这篇调查论文从概念上和实践上对自然语言处理（NLP）领域的自然语言推理提出了更清晰的看法。从概念上讲，我们基于哲学和 NLP 场景，为 NLP 中的自然语言推理提供了明确的定义，讨论了哪些类型的任务需要推理，并引入了推理的分类法。实际上，我们对 NLP 中的自然语言推理进行了全面的文献综述，主要涵盖经典逻辑推理、自然语言推理、多跳问答和常识推理。该论文还识别并看待了后向推理，这是一种强大的多步推理范式，并将可废止推理介绍为自然语言推理研究未来最重要的方向之一。我们专注于单模态非结构化自然语言文本，不包括神经符号研究和数学推理。