mlsanityData to 7 Oct 2026

QAID: Question Answering Inspired Few-shot Intent Detection

arXiv 2303.01593 ↗First posted 2 Mar 2023cs.CL cs.AI cs.IRcited by 5

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Abstract (arXiv)

Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. To that end, we utilize a question-answering retrieval architecture and adopt a two stages training schema with batch contrastive loss. In the pre-training stage, we improve query representations through self-supervised training. Then, in the fine-tuning stage, we increase contextualized token-level similarity scores between queries and answers from the same intent. Our results on three few-shot intent detection benchmarks achieve state-of-the-art performance.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 31 of its 46 references that are in the map. First posted on arXiv 2 Mar 2023.

Builds on

31 of 46 references.

References · 31

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  10. 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 378
  11. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 299
  12. 2020Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningcited by 275
19 more
  1. 2019Multilingual Universal Sentence Encoder for Semantic Retrievalcited by 192
  2. 2020CLEAR: Contrastive Learning for Sentence Representationcited by 186
  3. 2020TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialoguecited by 185
  4. 2019Benchmarking Natural Language Understanding Services for building Conversational Agentscited by 166
  5. 2022Efficient Few-Shot Learning Without Promptscited by 135
  6. 2021Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collectionscited by 128
  7. 2020DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialoguecited by 89
  8. 2021Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoderscited by 88
  9. 2020Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inferencecited by 47
  10. 2021Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuningcited by 46
  11. 2021Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformercited by 45
  12. 2021Effectiveness of Pre-training for Few-shot Intent Classificationcited by 27
  13. 2020Example-Driven Intent Prediction with Observerscited by 26
  14. 2022Zero-Shot Text Classification with Self-Trainingcited by 26
  15. 2022Where to start? Analyzing the potential value of intermediate modelscited by 23
  16. 2022Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropizationcited by 19
  17. 2022Cluster & Tune: Boost Cold Start Performance in Text Classificationcited by 13
  18. 2022Lexical Generalization Improves with Larger Models and Longer Trainingcited by 9
  19. 2020Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluationscited by 8

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Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2024Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classificationcited by 6
  2. 2024When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classescited by 2
  3. 2024A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLUcited by 2
  4. 2023RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Trainingcited by 1
  5. 2025Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detectioncited by 0