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Natural Language Reasoning, A Survey
The Chinese University Of Hong Kong, Shenzhen, China.
The Chinese University of Hong Kong, Shenzhen, China.
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-2851-4260
The Chinese University of Hong Kong, Shenzhen, China.ORCID iD: 0000-0002-1501-9914
2024 (English)In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 56, no 12, p. 1-39, article id 304Article in journal (Refereed) Published
Abstract [en]

This survey article proposes a clearer view of Natural Language Reasoning (NLR) in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct definition for NLR 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 NLR in NLP, mainly covering classical logical reasoning, Natural Language Inference (NLI), multi-hop question answering, and commonsense reasoning. The article 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 NLR research. We focus on single-modality unstructured natural language text, excluding neuro-symbolic research and mathematical reasoning. © 2024 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2024. Vol. 56, no 12, p. 1-39, article id 304
Keywords [en]
Natural language reasoning, pre-trained language models
National Category
Natural Language Processing
Identifiers
URN: urn:nbn:se:hh:diva-54856DOI: 10.1145/3664194Scopus ID: 2-s2.0-85206324959OAI: oai:DiVA.org:hh-54856DiVA, id: diva2:1911956
Available from: 2024-11-11 Created: 2024-11-11 Last updated: 2025-10-01Bibliographically approved

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Tiwari, Prayag

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