BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses
<|reference_start|>BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses: 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.<|reference_end|>
@article{zeng2024boottod:,
    title={BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning
  Diverse Responses},
    author={Weihao Zeng, Keqing He, Yejie Wang, Dayuan Fu, Weiran Xu},
    journal={arXiv preprint arXiv:2403.01163},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01163},
    primaryClass={cs.CL}
}