# Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents

arXiv: https://arxiv.org/abs/1910.03487 · HTML: https://mlsanity.com/paper/1910.03487/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

## Built on by (most cited first, 21 of 21)

- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [A Comprehensive Survey on Data Augmentation](https://mlsanity.com/paper/2405.09591/) (2024)
- [LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging](https://mlsanity.com/paper/2209.09900/) (2022)
- [Prompt Perturbation Consistency Learning for Robust Language Models](https://mlsanity.com/paper/2402.15833/) (2024)
- [BOLT: Fast Energy-based Controlled Text Generation with Tunable Biases](https://mlsanity.com/paper/2305.12018/) (2023)
- [TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding](https://mlsanity.com/paper/2205.06153/) (2022)
- [Privacy-Preserving Domain Adaptation of Semantic Parsers](https://mlsanity.com/paper/2212.10520/) (2022)
- [Pseudo Siamese Network for Few-shot Intent Generation](https://mlsanity.com/paper/2105.00896/) (2021)
- [Graphine: A Dataset for Graph-aware Terminology Definition Generation](https://mlsanity.com/paper/2109.04018/) (2021)
- [Diversity-oriented Data Augmentation with Large Language Models](https://mlsanity.com/paper/2502.11671/) (2025)
- [Enhancing In-Context Learning via Implicit Demonstration Augmentation](https://mlsanity.com/paper/2407.00100/) (2024)
- [Distributional Data Augmentation Methods for Low Resource Language](https://mlsanity.com/paper/2309.04862/) (2023)
- [Linguistically-Controlled Paraphrase Generation](https://mlsanity.com/paper/2410.24199/) (2024)
- [Topical Language Generation using Transformers](https://mlsanity.com/paper/2103.06434/) (2021)
- [Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems](https://mlsanity.com/paper/2306.04823/) (2023)
- [CALICO: Conversational Agent Localization via Synthetic Data Generation](https://mlsanity.com/paper/2412.05388/) (2024)
- [TARDiS : Text Augmentation for Refining Diversity and Separability](https://mlsanity.com/paper/2501.02739/) (2025)
- [Structure-Preserving Document-Level Data Augmentation via Discourse Parsing](https://mlsanity.com/paper/2610.19497/) (2026)

## Abstract (arXiv)

Data availability is a bottleneck during early stages of development of new capabilities for intelligent artificial agents. We investigate the use of text generation techniques to augment the training data of a popular commercial artificial agent across categories of functionality, with the goal of faster development of new functionality. We explore a variety of encoder-decoder generative models for synthetic training data generation and propose using conditional variational auto-encoders. Our approach requires only direct optimization, works well with limited data and significantly outperforms the previous controlled text generation techniques. Further, the generated data are used as additional training samples in an extrinsic intent classification task, leading to improved performance by up to 5% absolute f-score in low-resource cases, validating the usefulness of our approach.