Knowledge Synergy Complement Learning for Multi-Skill Dialogue Generation

The field of multi-skill dialogue generation focuses on creating conversational agents capable of handling a variety of tasks. Traditional approaches, such as task detection and adapter fusion, have faced challenges; task detection heavily depends on accurate context recognition, while adapter fusion methods often suffer from high computational complexity and are constrained by the performance of single-adapter models. To address these issues, we propose a novel method called Knowledge Synergy Complement (KSC) learning for enhancing multi-skill dialogue generation. In KSC, we leverage single-skill adapters by defining their Lipschitz constants as macro knowledge and utilizing intermediate hidden states to capture micro knowledge. This complementary approach allows for better integration of diverse skills. Moreover, a novel knowledge pruning mechanism is introduced to prevent knowledge forgetting and reduce conflict between skills. Our experiments demonstrate that KSC effectively integrates multiple skills, achieving superior performance compared to current state-of-the-art methods, while also being more efficient in terms of computational and storage resources required.