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Composed Variational Natural Language Generation for Few-shot Intents

arXiv 2009.10056 ↗First posted 21 Sep 2020cs.CLcited by 19

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Abstract (arXiv)

In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario. To build connections between existing many-shot intents and few-shot intents, we consider an intent as a combination of a domain and an action, and propose a composed variational natural language generator (CLANG), a transformer-based conditional variational autoencoder. CLANG utilizes two latent variables to represent the utterances corresponding to two different independent parts (domain and action) in the intent, and the latent variables are composed together to generate natural examples. Additionally, to improve the generator learning, we adopt the contrastive regularization loss that contrasts the in-class with the out-of-class utterance generation given the intent. To evaluate the quality of the generated utterances, experiments are conducted on the generalized few-shot intent detection task. Empirical results show that our proposed model achieves state-of-the-art performances on two real-world intent detection datasets.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Sep 2020.

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Citing papers per year: 2021: 9; 2022: 5; 2023: 3; 2024: 2.

19 papers.

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  1. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 185
  2. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  3. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  4. 2021Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuningcited by 46
  5. 2021Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformercited by 45
  6. 2021Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and Systemcited by 30
  7. 2021Effectiveness of Pre-training for Few-shot Intent Classificationcited by 27
  8. 2022Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropizationcited by 19
  9. 2022Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue Systemcited by 16
  10. 2021Pseudo Siamese Network for Few-shot Intent Generationcited by 11
  11. 2021A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detectioncited by 5
  12. 2022Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation in Few Shotscited by 5
7 more
  1. 2022Template-based Approach to Zero-shot Intent Recognitioncited by 5
  2. 2023Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-trainingcited by 5
  3. 2023Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generationcited by 1
  4. 2024A Unified Causal View of Instruction Tuningcited by 1
  5. 2021Exploring the Limits of Natural Language Inference Based Setup for Few-Shot Intent Detectioncited by 0
  6. 2023Inducing Causal Structure for Abstractive Text Summarizationcited by 0
  7. 2024Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documentscited by 0