Domain Adaptation in Intent Classification Systems: A Review

TL;DR

This paper systematically reviews intent classification systems in dialogue agents, analyzing datasets, methods, and challenges in domain adaptation to guide future NLP research.

Contribution

It provides a comprehensive technical review of intent classification, highlighting limitations and opportunities for improving domain adaptation in dialogue systems.

Findings

Intent classification remains challenging due to domain variability.

Many datasets and methods lack clarity in implementation details.

Opportunities exist for developing more adaptable and robust intent classifiers.

Abstract

Dialogue agents, which perform specific tasks, are part of the long-term goal of NLP researchers to build intelligent agents that communicate with humans in natural language. Such systems should adapt easily from one domain to another to assist users in completing tasks. Researchers have developed a broad range of techniques, objectives, and datasets for intent classification to achieve such systems. Despite the progress in developing intent classification systems (ICS), a systematic review of the progress from a technical perspective is yet to be conducted. In effect, important implementation details of intent classification remain restricted and unclear, making it hard for natural language processing (NLP) researchers to develop new methods. To fill this gap, we review contemporary works in intent classification. Specifically, we conduct a thorough technical review of the datasets, domains, tasks, and methods needed to train the intent classification part of dialogue systems. Our structured analysis describes why intent classification is difficult and studies the limitations to domain adaptation while presenting opportunities for future work.