综述常识推理与意图识别前沿，揭示多模态、跨文化模型发展趋势
An Interdisciplinary Review of Commonsense Reasoning and Intent Detection
- 整合28篇顶会论文，按方法与应用分类梳理
- 提出面向多语言、上下文感知的自适应模型新方向
- 适合关注NLP与人机交互融合的研究者参考
本文综述了2020至2025年间发表于ACL、EMNLP和CHI的28篇论文，系统探讨常识推理与意图检测两大自然语言理解核心挑战。从零样本学习、文化适配、结构化评估到交互式场景，全面分析常识推理进展；在意图检测方面，涵盖开放集模型、生成式建模、聚类方法及以用户为中心的系统设计。通过融合自然语言处理与人机交互的洞见，指出当前研究在可解释性、泛化能力与基准构建方面的关键缺口，并强调向更适应性、多语言、上下文敏感模型演进的趋势。
原文摘要 · Abstract (English)
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.
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