Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups

TL;DR

This paper introduces a data augmentation framework that uses large language models to generate personalized dialogue data, improving spoken dialogue systems' performance for low-resource user groups like minors.

Contribution

The study presents a novel approach combining LLMs and PLMs to augment dialogue data, enabling SDSs to better adapt to users with limited data, especially minors.

Findings

Enhanced SDS performance with augmented data

Improved interaction quality for low-resource user groups

Validated effectiveness through extensive experiments

Abstract

This study addresses the interaction challenges encountered by spoken dialogue systems (SDSs) when engaging with users who exhibit distinct conversational behaviors, particularly minors, in scenarios where data are scarce. We propose a novel data augmentation framework to enhance SDS performance for user groups with limited resources. Our approach leverages a large language model (LLM) to extract speaker styles and a pre-trained language model (PLM) to simulate dialogue act history. This method generates enriched and personalized dialogue data, facilitating improved interactions with unique user demographics. Extensive experiments validate the efficacy of our methodology, highlighting its potential to foster the development of more adaptive and inclusive dialogue systems.