mlsanityData to 14 Oct 2026

Domain Adaptation in Intent Classification Systems: A Review

arXiv 2404.14415 ↗First posted 26 Mar 2024cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 61 of its 75 references that are in the map. First posted on arXiv 26 Mar 2024.

Builds on

61 of 75 references.

References · 61

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38 more
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50 most cited of 61 All 61 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Deep Learning Approaches for Multimodal Intent Recognition: A Surveycited by 6
  2. 2026Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answeringcited by 2
  3. 2026How DDAIR you? Disambiguated Data Augmentation for Intent Recognitioncited by 1
  4. 2026IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inferencecited by 0