TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation
Yujie Feng, Xu Chu, Yongxin Xu, Guangyuan Shi, Bo Liu, Xiao-Ming Wu · 2024
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 1 is provided for reproducibility.