mlsanityData to 14 Oct 2026

Data Augmentation for Intent Classification

arXiv 2206.05790 ↗First posted 12 Jun 2022cs.CLcited by 3

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Abstract (arXiv)

Training accurate intent classifiers requires labeled data, which can be costly to obtain. Data augmentation methods may ameliorate this issue, but the quality of the generated data varies significantly across techniques. We study the process of systematically producing pseudo-labeled data given a small seed set using a wide variety of data augmentation techniques, including mixing methods together. We find that while certain methods dramatically improve qualitative and quantitative performance, other methods have minimal or even negative impact. We also analyze key considerations when implementing data augmentation methods in production.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 12 Jun 2022.

Builds on

16 of 21 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
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  10. 2019Good-Enough Compositional Data Augmentationcited by 158
  11. 2020SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustnesscited by 96
  12. 2020Paraphrase Augmented Task-Oriented Dialog Generationcited by 43
4 more
  1. 2018Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generationcited by 34
  2. 2019GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errorscited by 22
  3. 2019Automatically Learning Data Augmentation Policies for Dialogue Taskscited by 20
  4. 2021GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentationcited by 20

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.

  1. 2023Mixture of Soft Prompts for Controllable Data Generationcited by 23
  2. 2023Exploring Zero and Few-shot Techniques for Intent Classificationcited by 21
  3. 2022Sources of Noise in Dialogue and How to Deal with Themcited by 1