An Interdisciplinary Review of Commonsense Reasoning and Intent Detection
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913897962799104 |
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| author | Sakib, Md Nazmus |
| author_facet | Sakib, Md Nazmus |
| contents | This review explores recent advances in commonsense reasoning and intent detection, two key challenges in natural language understanding. We analyze 28 papers from ACL, EMNLP, and CHI (2020-2025), organizing them by methodology and application. Commonsense reasoning is reviewed across zero-shot learning, cultural adaptation, structured evaluation, and interactive contexts. Intent detection is examined through open-set models, generative formulations, clustering, and human-centered systems. By bridging insights from NLP and HCI, we highlight emerging trends toward more adaptive, multilingual, and context-aware models, and identify key gaps in grounding, generalization, and benchmark design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14040 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Interdisciplinary Review of Commonsense Reasoning and Intent Detection Sakib, Md Nazmus Computation and Language Human-Computer Interaction This review explores recent advances in commonsense reasoning and intent detection, two key challenges in natural language understanding. We analyze 28 papers from ACL, EMNLP, and CHI (2020-2025), organizing them by methodology and application. Commonsense reasoning is reviewed across zero-shot learning, cultural adaptation, structured evaluation, and interactive contexts. Intent detection is examined through open-set models, generative formulations, clustering, and human-centered systems. By bridging insights from NLP and HCI, we highlight emerging trends toward more adaptive, multilingual, and context-aware models, and identify key gaps in grounding, generalization, and benchmark design. |
| title | An Interdisciplinary Review of Commonsense Reasoning and Intent Detection |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.14040 |