# Paraphrase Augmented Task-Oriented Dialog Generation

arXiv: https://arxiv.org/abs/2004.07462 · HTML: https://mlsanity.com/paper/2004.07462/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 30 of 43)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection](https://mlsanity.com/paper/2111.14592/) (2021)
- [Evaluating Large Language Models on Controlled Generation Tasks](https://mlsanity.com/paper/2310.14542/) (2023)
- [Transferable Dialogue Systems and User Simulators](https://mlsanity.com/paper/2107.11904/) (2021)
- [Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding](https://mlsanity.com/paper/2210.14169/) (2022)
- [AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation](https://mlsanity.com/paper/2106.05589/) (2021)
- [BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset](https://mlsanity.com/paper/2210.05109/) (2022)
- [PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation](https://mlsanity.com/paper/2208.08110/) (2022)
- [Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems](https://mlsanity.com/paper/2109.04314/) (2021)
- [Enrich and Detect: Video Temporal Grounding with Multimodal LLMs](https://mlsanity.com/paper/2510.17023/) (2025)
- [TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations](https://mlsanity.com/paper/2112.12441/) (2021)
- [GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation](https://mlsanity.com/paper/2211.10330/) (2022)
- [DG2: Data Augmentation Through Document Grounded Dialogue Generation](https://mlsanity.com/paper/2112.08342/) (2021)
- [Toward Self-learning End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/2201.06849/) (2022)
- [A Survey of Data Synthesis Approaches](https://mlsanity.com/paper/2407.03672/) (2024)
- [Retrieve & Memorize: Dialog Policy Learning with Multi-Action Memory](https://mlsanity.com/paper/2106.02317/) (2021)
- [Nearest Neighbour Few-Shot Learning for Cross-lingual Classification](https://mlsanity.com/paper/2109.02221/) (2021)
- [Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review](https://mlsanity.com/paper/2505.04531/) (2025)
- [End-to-End Conversational Search for Online Shopping with Utterance Transfer](https://mlsanity.com/paper/2109.05460/) (2021)
- [N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking](https://mlsanity.com/paper/2103.00293/) (2021)
- [ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair](https://mlsanity.com/paper/2205.04651/) (2022)
- [Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding](https://mlsanity.com/paper/2109.01583/) (2021)
- [Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems](https://mlsanity.com/paper/2204.06452/) (2022)
- [Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset](https://mlsanity.com/paper/2209.13464/) (2022)
- [Unsupervised Syntactically Controlled Paraphrase Generation with Abstract Meaning Representations](https://mlsanity.com/paper/2211.00881/) (2022)
- [PIP: Parse-Instructed Prefix for Syntactically Controlled Paraphrase Generation](https://mlsanity.com/paper/2305.16701/) (2023)
- [SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching](https://mlsanity.com/paper/2110.11514/) (2021)
- [Data Augmentation for Intent Classification](https://mlsanity.com/paper/2206.05790/) (2022)
- [Data Augmentation for Conversational AI](https://mlsanity.com/paper/2309.04739/) (2023)

## Abstract (arXiv)

Neural generative models have achieved promising performance on dialog generation tasks if given a huge data set. However, the lack of high-quality dialog data and the expensive data annotation process greatly limit their application in real-world settings. We propose a paraphrase augmented response generation (PARG) framework that jointly trains a paraphrase model and a response generation model to improve the dialog generation performance. We also design a method to automatically construct paraphrase training data set based on dialog state and dialog act labels. PARG is applicable to various dialog generation models, such as TSCP (Lei et al., 2018) and DAMD (Zhang et al., 2019). Experimental results show that the proposed framework improves these state-of-the-art dialog models further on CamRest676 and MultiWOZ. PARG also significantly outperforms other data augmentation methods in dialog generation tasks, especially under low resource settings.