# A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification

arXiv: https://arxiv.org/abs/1910.04176 · HTML: https://mlsanity.com/paper/1910.04176/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 30 of 45)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Neural Data Augmentation via Example Extrapolation](https://mlsanity.com/paper/2102.01335/) (2021)
- [Data Augmentation for Intent Classification with Off-the-shelf Large Language Models](https://mlsanity.com/paper/2204.01959/) (2022)
- [Controllable Semantic Parsing via Retrieval Augmentation](https://mlsanity.com/paper/2110.08458/) (2021)
- [SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification](https://mlsanity.com/paper/2211.16191/) (2022)
- [FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://mlsanity.com/paper/2108.06332/) (2021)
- [Mask-guided BERT for Few Shot Text Classification](https://mlsanity.com/paper/2302.10447/) (2023)
- [PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation](https://mlsanity.com/paper/2310.14192/) (2023)
- [Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information](https://mlsanity.com/paper/2302.05096/) (2023)
- [Using GPT-4 to Augment Unbalanced Data for Automatic Scoring](https://mlsanity.com/paper/2310.18365/) (2023)
- [Learning Multimodal Data Augmentation in Feature Space](https://mlsanity.com/paper/2212.14453/) (2022)
- [Contrastive Learning for Prompt-Based Few-Shot Language Learners](https://mlsanity.com/paper/2205.01308/) (2022)
- [Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System](https://mlsanity.com/paper/2205.04006/) (2022)
- [Hallucination Improves the Performance of Unsupervised Visual Representation Learning](https://mlsanity.com/paper/2307.12168/) (2023)
- [Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data](https://mlsanity.com/paper/2109.10856/) (2021)
- [Opponent Modeling in Negotiation Dialogues by Related Data Adaptation](https://mlsanity.com/paper/2205.00344/) (2022)
- [A Feature-space Multimodal Data Augmentation Technique for Text-video Retrieval](https://mlsanity.com/paper/2208.02080/) (2022)
- [Frozen Feature Augmentation for Few-Shot Image Classification](https://mlsanity.com/paper/2403.10519/) (2024)
- [ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning](https://mlsanity.com/paper/2106.08523/) (2021)
- [PAC-tuning:Fine-tuning Pretrained Language Models with PAC-driven Perturbed Gradient Descent](https://mlsanity.com/paper/2310.17588/) (2023)
- [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://mlsanity.com/paper/2406.04215/) (2024)
- [ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification](https://mlsanity.com/paper/2101.11753/) (2021)
- [Exploring Empty Spaces: Human-in-the-Loop Data Augmentation](https://mlsanity.com/paper/2410.01088/) (2024)
- [Good-Enough Example Extrapolation](https://mlsanity.com/paper/2109.05602/) (2021)
- [Embedding Hallucination for Few-Shot Language Fine-tuning](https://mlsanity.com/paper/2205.01307/) (2022)
- [AugCSE: Contrastive Sentence Embedding with Diverse Augmentations](https://mlsanity.com/paper/2210.13749/) (2022)
- [CST5: Data Augmentation for Code-Switched Semantic Parsing](https://mlsanity.com/paper/2211.07514/) (2022)
- [Boosting Few-Shot Text Classification via Distribution Estimation](https://mlsanity.com/paper/2303.16764/) (2023)

## Abstract (arXiv)

New conversation topics and functionalities are constantly being added to conversational AI agents like Amazon Alexa and Apple Siri. As data collection and annotation is not scalable and is often costly, only a handful of examples for the new functionalities are available, which results in poor generalization performance. We formulate it as a Few-Shot Integration (FSI) problem where a few examples are used to introduce a new intent. In this paper, we study six feature space data augmentation methods to improve classification performance in FSI setting in combination with both supervised and unsupervised representation learning methods such as BERT. Through realistic experiments on two public conversational datasets, SNIPS, and the Facebook Dialog corpus, we show that data augmentation in feature space provides an effective way to improve intent classification performance in few-shot setting beyond traditional transfer learning approaches. In particular, we show that (a) upsampling in latent space is a competitive baseline for feature space augmentation (b) adding the difference between two examples to a new example is a simple yet effective data augmentation method.