Abstract
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.
Abstract (translated)
这篇综述探讨了常识推理和意图检测在自然语言理解中的最新进展，这两项是该领域的关键挑战。我们分析了从ACL、EMNLP和CHI（2020-2025）中选出的28篇论文，并按方法论和应用领域对其进行分类。对于常识推理，综述涵盖了零样本学习、文化适应、结构化评估以及互动场景的方法；而对于意图检测，则通过开放集模型、生成式公式、聚类分析及以用户为中心的设计进行探讨。本文结合了自然语言处理（NLP）与人机交互（HCI）领域的见解，强调了向更具适应性、多语种和情境感知的模型发展的新趋势，并指出了在实证基础、泛化能力和基准测试设计方面存在的关键缺口。
URL
https://arxiv.org/abs/2506.14040
