Natural Language Reasoning, A Survey
It addresses the need for a clearer conceptual and practical framework for researchers in NLP, but it is incremental as it synthesizes existing work rather than introducing new methods.
This survey paper tackles the problem of clarifying natural language reasoning in NLP by providing a distinct definition, taxonomy, and comprehensive literature review, covering tasks like logical reasoning and question answering, and identifying future directions such as defeasible reasoning.
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 techniques and mathematical reasoning.