Task-oriented dialogue systems have been a promising area in the NLP field. Previous work showed the effectiveness of using a single GPT-2 based model to predict belief states and responses via causal language modeling. In this paper, we leverage multi-task learning techniques to train a GPT-2 based model on a more challenging dataset with multiple domains, multiple modalities, and more diversity in output formats. Using only a single model, our method achieves better performance on all sub-tasks, across domains, compared to task and domain-specific models. Furthermore, we evaluated several proposed strategies for GPT-2 based dialogue systems with comprehensive ablation studies, showing that all techniques can further improve the performance.
@article{arxiv.2110.05221,
title = {Multi-Task Learning for Situated Multi-Domain End-to-End Dialogue Systems},
author = {Po-Nien Kung and Chung-Cheng Chang and Tse-Hsuan Yang and Hsin-Kai Hsu and Yu-Jia Liou and Yun-Nung Chen},
journal= {arXiv preprint arXiv:2110.05221},
year = {2021}
}