Advancing Dialogue Systems in Low-Resource Conditions: A Synthetic Data Generation Approach
Abstract
This thesis examines how to improve low-resource dialogue systems using a synthetic data approach. It focuses on three types: flowchart-based systems that guide users through structured steps, multi-modal systems that combine images and text and socially-aware systems that follow social norms. New methods create synthetic data to fill gaps, cover rare situations, and link visual and textual information. New datasets help systems learn and apply social norms.
Fields of Study & Subjects
Speech and dialogue systems
AI in Service Interactions
Intelligent Tutoring Systems and Adaptive Learning
Publication details
- Journal / source
- Monash University
- Publication date
- 26 February 2026
- Language
- EN
- Document type
- Thesis