🤖 AI Summary
This paper addresses two core challenges in natural language understanding: commonsense reasoning and intent detection. Through a cross-disciplinary literature review of 28 representative papers published at ACL, EMNLP, and CHI between 2020–2025, we apply systematic classification and thematic analysis—uniquely integrating NLP and HCI perspectives for the first time. We identify four commonsense reasoning pathways (zero-shot learning, cultural adaptation, structured evaluation, and interactive modeling) and four intent detection paradigms (open-set modeling, generative identification, unsupervised clustering, and human-centered design). Our analysis reveals three critical gaps: insufficient semantic grounding, weak cross-scenario generalization, and inconsistent benchmarking practices. To bridge these, we propose an evolutionary framework centered on adaptability, multilinguality, and context awareness. This framework provides both theoretical foundations and actionable guidelines for developing next-generation AI systems that are interpretable, robust, and human-centered.
📝 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.