Multi-Task Learning for Situated Multi-Domain End-to-End Dialogue Systems

• URL: http://arxiv.org/abs/2110.05221v1
• Date: Mon, 11 Oct 2021 12:36:30 GMT
• Title: Multi-Task Learning for Situated Multi-Domain End-to-End Dialogue Systems
• Authors: Po-Nien Kung, Chung-Cheng Chang, Tse-Hsuan Yang, Hsin-Kai Hsu, Yu-Jia Liou, Yun-Nung Chen
• Abstract summary: We leverage multi-task learning techniques to train a GPT-2 based model on a more challenging dataset. Our method achieves better performance on all sub-tasks, across domains, compared to task and domain-specific models.
• Score: 21.55075825370981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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