# Data Augmentation with Atomic Templates for Spoken Language Understanding

arXiv: https://arxiv.org/abs/1908.10770 · HTML: https://mlsanity.com/paper/1908.10770/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2019.

## 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)
- [Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills](https://mlsanity.com/paper/2107.07261/) (2021)
- [AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation](https://mlsanity.com/paper/2106.05589/) (2021)
- [Privacy-Preserving Domain Adaptation of Semantic Parsers](https://mlsanity.com/paper/2212.10520/) (2022)
- [Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction](https://mlsanity.com/paper/2301.02427/) (2023)
- [Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation](https://mlsanity.com/paper/2205.08675/) (2022)
- [Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding](https://mlsanity.com/paper/2109.01583/) (2021)
- [Contextual Data Augmentation for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2310.10380/) (2023)
- [Compositional Generalization in Spoken Language Understanding](https://mlsanity.com/paper/2312.15815/) (2023)

## Abstract (arXiv)

Spoken Language Understanding (SLU) converts user utterances into structured semantic representations. Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, especially when domain changes or a new domain comes. In this work, we propose a data augmentation method with atomic templates for SLU, which involves minimum human efforts. The atomic templates produce exemplars for fine-grained constituents of semantic representations. We propose an encoder-decoder model to generate the whole utterance from atomic exemplars. Moreover, the generator could be transferred from source domains to help a new domain which has little data. Experimental results show that our method achieves significant improvements on DSTC 2&3 dataset which is a domain adaptation setting of SLU.