# Contextual Data Augmentation for Task-Oriented Dialog Systems

arXiv: https://arxiv.org/abs/2310.10380 · HTML: https://mlsanity.com/paper/2310.10380/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://mlsanity.com/paper/1508.01745/) (2015)
- [UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2](https://mlsanity.com/paper/2012.03539/) (2020)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [Building a Conversational Agent Overnight with Dialogue Self-Play](https://mlsanity.com/paper/1801.04871/) (2018)
- [Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding](https://mlsanity.com/paper/1807.01554/) (2018)
- [Paraphrase Augmented Task-Oriented Dialog Generation](https://mlsanity.com/paper/2004.07462/) (2020)
- [LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization](https://mlsanity.com/paper/2011.09378/) (2020)
- [AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation](https://mlsanity.com/paper/2106.05589/) (2021)
- [Latent Intention Dialogue Models](https://mlsanity.com/paper/1705.10229/) (2017)
- [Data Augmentation with Atomic Templates for Spoken Language Understanding](https://mlsanity.com/paper/1908.10770/) (2019)
- [Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems](https://mlsanity.com/paper/2011.08243/) (2020)
- [Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models](https://mlsanity.com/paper/2108.08451/) (2021)

## Built on by (most cited first, 2 of 2)

- [Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/2406.06127/) (2024)
- [MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues](https://mlsanity.com/paper/2502.17268/) (2025)

## Abstract (arXiv)

Collection of annotated dialogs for training task-oriented dialog systems have been one of the key bottlenecks in improving current models. While dialog response generation has been widely studied on the agent side, it is not evident if similar generative models can be used to generate a large variety of, and often unexpected, user inputs that real dialog systems encounter in practice. Existing data augmentation techniques such as paraphrase generation do not take the dialog context into consideration. In this paper, we develop a novel dialog augmentation model that generates a user turn, conditioning on full dialog context. Additionally, with a new prompt design for language model, and output re-ranking, the dialogs generated from our model can be directly used to train downstream dialog systems. On common benchmark datasets MultiWoZ and SGD, we show that our dialog augmentation model generates high quality dialogs and improves dialog success rate by as much as 8% over baseline.