T a SL : Continual Dialog State Tracking via Task Skill Localization and Consolidation (2024)

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, with a 7.6% absolute increase in Avg. JGA and an 11% absolute rise in BWT metrics over existing state-of-the-art methods. The source code is provided for reproducibility.

## introduction with the rising popularity of conversational digital assistants, it is imperative for dialogue systems to integrate new services while sustaining proficiency in prior tasks seamlessly. traditional research, often conducted within specific domains offline, falls short in adaptability to new scenarios @xcite . retraining pre-trained language models (plms) from scratch is both challenging and resource-intensive @xcite , highlighting the necessity for efficient continual learning (cl) approaches in dialogue systems @xcite . dialogue state tracking (dst), crucial for taskoriented dialogue systems, dynamically updates (domain, slot, value) triplets to capture user intentions precisely. the urgent demand for advancing dst models to accommodate emerging services has catalyzed the development of the continual dst task @xcite . an effective continual dst system must address the issue of catastrophic forgetting @xcite , where a model's proficiency in old tasks diminishes after learning new ones. it should also promote knowledge transfer (kt) @xcite across domains foot_0 to enhance end-task performances. knowledge transfer includes forward transfer, which improves new task performance using knowledge from previous tasks, and backward transfer, which enhances performance on previous tasks after learning a new relevant task. striking a balance between retaining previous knowledge and excelling in new tasks is vital for success. however, current continual dst methods @xcite @xcite ) mainly focus on mitigating forgetting through memory replay or regularization, overlooking the advantages of kt that can be derived from the inherent correlations between different dst domains. task correlation in continual dst is quite ev-ident. for instance, domains like "hotel" and "restaurant" share semantically similar slots, such as "area" and "bookday", highlighting the need for models to identify and handle common information types. the similarity in these domain-shared slots is crucial for enabling kt. however, learning domain-specific slots like "food" for "restaurant" could introduce unique information that disrupts the retention of previously acquired knowledge, leading to catastrophic forgetting. to address these challenges, we introduce task skill localization and consolidation (tasl), a framework designed to improve kt between tasks without relying on memory replay. this is achieved by identifying and consolidating the importance distribution of model parameters across tasks. tasl initially employs a group-wise importanceaware skill localization technique that utilizes gradient trajectories to pinpoint tiny regions in the model that store crucial knowledge for the current task. by comparing the importance distribution with those of previous tasks, we can differentiate between task-specific and task-shared parameters, as illustrated in figure 1 . our innovative skill consolidation phase then categorically integrates weights from previous tasks with the current one, enabling effective kt while minimizing forgetting. in detail, the importance-aware skill localization method employs a new group-wise metric to compute importance scores, effectively quantifying the significance of each "skill unit" foot_1 for the current task. our approach, focusing on parameter space rather than dataset-driven categorization of domain-shared and domain-specific slots, offers a more robust solution that accurately identifies taskspecific and task-shared knowledge, overcoming inaccuracies caused by dataset noise. our skill consolidation stage, then based on a fine-grained model averaging strategy, effectively manages different types of knowledge. we enable forward kt to new tasks by starting with a model initialized with weights from previously fine-tuned tasks, thus using past knowledge to improve learning for new tasks without restrictions. for backward kt, we merge knowledge from both current and past accumulated tasks into localized task-shared skill units, thereby enhancing their capability. to prevent catastrophic forgetting, we consolidate the integrity of skill units containing previous task-specific knowledge, ensuring they remain unaffected by new task learning. through extensive experiments on different parameter-level backbones (from 60m to 7b), tasl exhibits superior performance in mitigating forgetting and showcases remarkable capabilities for kt, significantly outperforming state-of-the-art methods. our main contributions include: • we propose a novel task skill localization and consolidation (tasl) framework for cl. jga and an 8.8% absolute boost in bwt metrics compared to previous sota methods. ## experiments and analysis dataset we use the continual learning for dst setup proposed by @xcite , which uses 15 single domains from the schema-guided dialog dataset (sgd) @xcite . we aggregate our results over the same five domain orders to make the most reliable comparisons with prior works. comparing results with the same order is crucial as the results can have significant variance depending on the chosen domains and their order. more details about data statistics, task selection, and orderings can be found in the appendix a. ## conclusion in this paper, we introduce a novel tasl method to enhance continual dst performance by facilitating effective knowledge transfer across tasks. our approach leverages an innovative importanceaware skill localization technique and a skill consolidation strategy to differentiate between domainspecific and domain-shared parameters, mitigating forgetting. comprehensive experiments showcase our method's exceptional ability to balance preserving past knowledge and excelling in new tasks.