An Interdisciplinary Review of Commonsense Reasoning and Intent Detection

TL;DR

This review synthesizes recent progress in commonsense reasoning and intent detection, emphasizing interdisciplinary approaches, emerging trends, and identifying key gaps in grounding, generalization, and benchmarking in NLP and HCI.

Contribution

It provides a comprehensive analysis of 28 recent papers, organizing advances by methodology and application, and bridges insights from NLP and HCI to highlight future research directions.

Findings

Emerging trend towards more adaptive, multilingual, and context-aware models.

Identification of key gaps in grounding, generalization, and benchmark design.

Analysis of diverse methodologies in commonsense reasoning and intent detection.

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.