# Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification

arXiv: https://arxiv.org/abs/2009.03695 · HTML: https://mlsanity.com/paper/2009.03695/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP](https://mlsanity.com/paper/2111.09618/) (2021)
- [Data Augmentation Methods for Anaphoric Zero Pronouns](https://mlsanity.com/paper/2109.09825/) (2021)
- [Learning towards Selective Data Augmentation for Dialogue Generation](https://mlsanity.com/paper/2303.09719/) (2023)
- [NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluation](https://mlsanity.com/paper/2111.05196/) (2021)
- [More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking](https://mlsanity.com/paper/2303.09905/) (2023)
- [Linguistically-Controlled Paraphrase Generation](https://mlsanity.com/paper/2410.24199/) (2024)
- [Cone: Unsupervised Contrastive Opinion Extraction](https://mlsanity.com/paper/2305.04599/) (2023)
- [Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/2406.06127/) (2024)

## Abstract (arXiv)

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models.