TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation

• URL: http://arxiv.org/abs/2408.09857v1
• Date: Mon, 19 Aug 2024 10:01:28 GMT
• Title: TaSL: Continual Dialog State Tracking via Task Skill Localization and Consolidation
• Authors: Yujie Feng, Xu Chu, Yongxin Xu, Guangyuan Shi, Bo Liu, Xiao-Ming Wu,
• Abstract summary: We present TaSL, a novel framework for task skill localization and consolidation. TaSL uses a novel group-wise technique to pinpoint task-specific and task-shared areas. As a result, TaSL strikes a balance between preserving previous knowledge and excelling at new tasks.
• Score: 14.533890076297393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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