A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation

• URL: http://arxiv.org/abs/2406.09881v2
• Date: Fri, 28 Jun 2024 12:58:11 GMT
• Title: A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
• Authors: Yongkang Liu, Ercong Nie, Shi Feng, Zheng Hua, Zifeng Ding, Daling Wang, Yifei Zhang, Hinrich Schütze,
• Abstract summary: Current state-of-the-art dialogue systems heavily rely on extensive training datasets. We propose a novel data textbfAugmentation framework for textbfMulti-textbfDomain textbfDialogue textbfGeneration, referred to as textbfAMD$2$G. The AMD$2$G framework consists of a data augmentation process and a two-stage training approach: domain-agnostic training and domain adaptation training.
• Score: 52.0964459842176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient or entirely absent. To tackle this challenge, we propose a novel data \textbf{A}ugmentation framework for \textbf{M}ulti-\textbf{D}omain \textbf{D}ialogue \textbf{G}eneration, referred to as \textbf{AMD$^2$G}. The AMD$^2$G framework consists of a data augmentation process and a two-stage training approach: domain-agnostic training and domain adaptation training. We posit that domain corpora are a blend of domain-agnostic and domain-specific features, with certain representation patterns shared among diverse domains. Domain-agnostic training aims to enable models to learn these common expressive patterns. To construct domain-agnostic dialogue corpora, we employ a \textit{\textbf{de-domaining}} data processing technique used to remove domain-specific features. By mitigating the effects of domain-specific features, the model trained on the de-domained corpora can effectively learn common expression patterns in different domains. Subsequently, we adapt the learned domain-agnostic features to the target domain through domain adaptation training. We conduct experiments on Chinese dialogue datasets from five different domains and show that AMD$^2$G achieves superior performance compared to both direct training on the target domain corpus and collective training on all five domain corpora. Our work underscores AMD$^2$G as a viable alternative solution for low-resource multi-domain dialogue generation. Code and data associated with our work are available on GitHub repository$^{\text 1}$.

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