# Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding

arXiv: https://arxiv.org/abs/1807.01554 · HTML: https://mlsanity.com/paper/1807.01554/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2018.

## Built on by (most cited first, 30 of 68)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://mlsanity.com/paper/2104.08826/) (2021)
- [Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://mlsanity.com/paper/2306.04140/) (2023)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks](https://mlsanity.com/paper/2202.12499/) (2022)
- [Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking](https://mlsanity.com/paper/2106.00291/) (2021)
- [SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems](https://mlsanity.com/paper/2110.06800/) (2021)
- [Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing](https://mlsanity.com/paper/2103.02227/) (2021)
- [FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://mlsanity.com/paper/2108.06332/) (2021)
- [Substructure Substitution: Structured Data Augmentation for NLP](https://mlsanity.com/paper/2101.00411/) (2021)
- [A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation](https://mlsanity.com/paper/2204.09867/) (2022)
- [GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation](https://mlsanity.com/paper/2109.03079/) (2021)
- [NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation](https://mlsanity.com/paper/2105.14454/) (2021)
- [Data Augmentation for Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2109.01758/) (2021)
- [Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning](https://mlsanity.com/paper/2103.12777/) (2021)
- [GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks](https://mlsanity.com/paper/2305.16663/) (2023)
- [Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://mlsanity.com/paper/2306.02842/) (2023)
- [Data Augmentation for Low-Resource Quechua ASR Improvement](https://mlsanity.com/paper/2207.06872/) (2022)
- [PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling](https://mlsanity.com/paper/2208.11508/) (2022)
- [Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play](https://mlsanity.com/paper/2210.12096/) (2022)
- [A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER](https://mlsanity.com/paper/2308.14533/) (2023)
- [Improving Black-box Robustness with In-Context Rewriting](https://mlsanity.com/paper/2402.08225/) (2024)
- [Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training](https://mlsanity.com/paper/2410.20796/) (2024)
- [Cross-Modal Generative Augmentation for Visual Question Answering](https://mlsanity.com/paper/2105.04780/) (2021)
- [SpliceOut: A Simple and Efficient Audio Augmentation Method](https://mlsanity.com/paper/2110.00046/) (2021)
- [Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues](https://mlsanity.com/paper/2210.16838/) (2022)
- [A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs](https://mlsanity.com/paper/2404.00303/) (2024)
- [N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking](https://mlsanity.com/paper/2103.00293/) (2021)
- [Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.07343/) (2021)
- [Text-To-Speech Data Augmentation for Low Resource Speech Recognition](https://mlsanity.com/paper/2204.00291/) (2022)

## Abstract (arXiv)

In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance without considering its relation with other utterances, we propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance's same semantic alternatives in the training data. A novel diversity rank is incorporated into the utterance representation to make the model produce diverse utterances and these diversely augmented utterances help to improve the language understanding module. Experimental results on the Airline Travel Information System dataset and a newly created semantic frame annotation on Stanford Multi-turn, Multidomain Dialogue Dataset show that our framework achieves significant improvements of 6.38 and 10.04 F-scores respectively when only a training set of hundreds utterances is represented. Case studies also confirm that our method generates diverse utterances.