BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses

• URL: http://arxiv.org/abs/2403.01163v1
• Date: Sat, 2 Mar 2024 10:34:11 GMT
• Title: BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses
• Authors: Weihao Zeng, Keqing He, Yejie Wang, Dayuan Fu, Weiran Xu
• Abstract summary: We propose a novel dialogue pre-training model called BootTOD. It learns task-oriented dialogue representations via a self-bootstrapping framework. BootTOD outperforms strong TOD baselines on diverse downstream dialogue tasks.
• Score: 24.79881150845294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained language models have been successful in many scenarios. However, their usefulness in task-oriented dialogues is limited due to the intrinsic linguistic differences between general text and task-oriented dialogues. Current task-oriented dialogue pre-training methods rely on a contrastive framework, which faces challenges such as selecting true positives and hard negatives, as well as lacking diversity. In this paper, we propose a novel dialogue pre-training model called BootTOD. It learns task-oriented dialogue representations via a self-bootstrapping framework. Unlike contrastive counterparts, BootTOD aligns context and context+response representations and dismisses the requirements of contrastive pairs. BootTOD also uses multiple appropriate response targets to model the intrinsic one-to-many diversity of human conversations. Experimental results show that BootTOD outperforms strong TOD baselines on diverse downstream dialogue tasks.

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