TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation
<|reference_start|>TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation: A practical dialogue system requires the capacity for ongoing skill acquisition and adaptability to new tasks while preserving prior knowledge. However, current methods for Continual Dialogue State Tracking (DST), a crucial function of dialogue systems, struggle with the catastrophic forgetting issue and knowledge transfer between tasks. We present TaSL, a novel framework for task skill localization and consolidation that enables effective knowledge transfer without relying on memory replay. TaSL uses a novel group-wise technique to pinpoint task-specific and task-shared areas. Additionally, a fine-grained skill consolidation strategy protects task-specific knowledge from being forgotten while updating shared knowledge for bi-directional knowledge transfer. As a result, TaSL strikes a balance between preserving previous knowledge and excelling at new tasks. Comprehensive experiments on various backbones highlight the significant performance improvements of TaSL over existing state-of-the-art methods. The source code is provided for reproducibility.<|reference_end|>
@article{feng2024tasl:,
    title={TaSL: Continual Dialog State Tracking via Task Skill Localization and
  Consolidation},
    author={Yujie Feng, Xu Chu, Yongxin Xu, Guangyuan Shi, Bo Liu, Xiao-Ming Wu},
    journal={arXiv preprint arXiv:2408.09857},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09857},
    primaryClass={cs.CL}
}