An Interdisciplinary Review of Commonsense Reasoning and Intent Detection
It synthesizes interdisciplinary insights from NLP and HCI to identify gaps in grounding, generalization, and benchmarks for researchers in these fields.
This review analyzes 28 recent papers on commonsense reasoning and intent detection in natural language understanding, organizing them by methodology and application to highlight trends toward adaptive, multilingual, and context-aware models.
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.