# Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning

arXiv: https://arxiv.org/abs/2103.07552 · HTML: https://mlsanity.com/paper/2103.07552/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 18 of 18)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization](https://mlsanity.com/paper/1807.00230/) (2018)
- [Good-Enough Compositional Data Augmentation](https://mlsanity.com/paper/1904.09545/) (2019)
- [Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules](https://mlsanity.com/paper/1905.05393/) (2019)
- [Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks](https://mlsanity.com/paper/1802.03796/) (2018)
- [Edinburgh Neural Machine Translation Systems for WMT 16](https://mlsanity.com/paper/1606.02891/) (2016)
- [SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation](https://mlsanity.com/paper/1808.07512/) (2018)
- [Attentive Pooling Networks](https://mlsanity.com/paper/1602.03609/) (2016)
- [Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning](https://mlsanity.com/paper/1605.03852/) (2016)
- [What Are People Asking About COVID-19? A Question Classification Dataset](https://mlsanity.com/paper/2005.12522/) (2020)
- [Rethinking Curriculum Learning with Incremental Labels and Adaptive Compensation](https://mlsanity.com/paper/2001.04529/) (2020)
- [Context-based Transformer Models for Answer Sentence Selection](https://mlsanity.com/paper/2006.01285/) (2020)

## Built on by (most cited first, 30 of 35)

- [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)
- [Data-Efficient Finetuning Using Cross-Task Nearest Neighbors](https://mlsanity.com/paper/2212.00196/) (2022)
- [RepLiQA: A Question-Answering Dataset for Benchmarking LLMs on Unseen Reference Content](https://mlsanity.com/paper/2406.11811/) (2024)
- [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)
- [Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications](https://mlsanity.com/paper/2306.11892/) (2023)
- [Imagination-Augmented Natural Language Understanding](https://mlsanity.com/paper/2204.08535/) (2022)
- [Contrastive Learning for Prompt-Based Few-Shot Language Learners](https://mlsanity.com/paper/2205.01308/) (2022)
- [Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning](https://mlsanity.com/paper/2109.05941/) (2021)
- [PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation](https://mlsanity.com/paper/2208.08110/) (2022)
- [Curricular Object Manipulation in LiDAR-based Object Detection](https://mlsanity.com/paper/2304.04248/) (2023)
- [Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM](https://mlsanity.com/paper/2304.11872/) (2023)
- [Evolving Knowledge Distillation with Large Language Models and Active Learning](https://mlsanity.com/paper/2403.06414/) (2024)
- [Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better](https://mlsanity.com/paper/2402.00263/) (2024)
- [NICE: To Optimize In-Context Examples or Not?](https://mlsanity.com/paper/2402.06733/) (2024)
- [Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models](https://mlsanity.com/paper/2306.09308/) (2023)
- [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)
- [SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training](https://mlsanity.com/paper/2210.12653/) (2022)
- [AugCSE: Contrastive Sentence Embedding with Diverse Augmentations](https://mlsanity.com/paper/2210.13749/) (2022)
- [One size doesn't fit all: Predicting the Number of Examples for In-Context Learning](https://mlsanity.com/paper/2403.06402/) (2024)
- [DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling](https://mlsanity.com/paper/2204.07679/) (2022)
- [BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction](https://mlsanity.com/paper/2302.06860/) (2023)
- [Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching](https://mlsanity.com/paper/2406.13361/) (2024)
- [Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models](https://mlsanity.com/paper/2407.13989/) (2024)
- [A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Ranking](https://mlsanity.com/paper/2210.15147/) (2022)
- [Data Augmentation for Neural NLP](https://mlsanity.com/paper/2302.11412/) (2023)
- [Colorful Cutout: Enhancing Image Data Augmentation with Curriculum Learning](https://mlsanity.com/paper/2403.20012/) (2024)
- [EXnet: Efficient In-context Learning for Data-less Text classification](https://mlsanity.com/paper/2305.14622/) (2023)

## Abstract (arXiv)

Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation -- a technique particularly suitable for training with limited data -- for this few-shot, highly-multiclass text classification setting. On four diverse text classification tasks, we find that common data augmentation techniques can improve the performance of triplet networks by up to 3.0% on average. To further boost performance, we present a simple training strategy called curriculum data augmentation, which leverages curriculum learning by first training on only original examples and then introducing augmented data as training progresses. We explore a two-stage and a gradual schedule, and find that, compared with standard single-stage training, curriculum data augmentation trains faster, improves performance, and remains robust to high amounts of noising from augmentation.