In this work, we study dialogue scenarios that start from chit-chat but eventually switch to task-related services, and investigate how a unified dialogue model, which can engage in both chit-chat and task-oriented dialogues, takes the initiative during the dialogue mode transition from chit-chat to task-oriented in a coherent and cooperative manner. We firstly build a {transition info extractor} (TIE) that keeps track of the preceding chit-chat interaction and detects the potential user intention to switch to a task-oriented service. Meanwhile, in the unified model, a {transition sentence generator} (TSG) is extended through efficient Adapter tuning and transition prompt learning. When the TIE successfully finds task-related information from the preceding chit-chat, such as a transition domain, then the TSG is activated automatically in the unified model to initiate this transition by generating a transition sentence under the guidance of transition information extracted by TIE. The experimental results show promising performance regarding the proactive transitions. We achieve an additional large improvement on TIE model by utilizing Conditional Random Fields (CRF). The TSG can flexibly generate transition sentences while maintaining the unified capabilities of normal chit-chat and task-oriented response generation.

翻译：本研究探讨了从闲聊起始但最终转向任务相关服务的对话场景，并研究如何使既能进行闲聊又能处理任务型对话的统一对话模型，在从闲聊到任务型对话模式转换过程中以连贯且合作的方式主动发起转换。我们首先构建了一个{转换信息提取器}（TIE），用于追踪先前的闲聊交互并检测用户潜在转向任务型服务的意图。同时，在统一模型中，通过高效的Adapter微调和转换提示学习扩展了{转换句子生成器}（TSG）。当TIE从先前闲聊中成功提取到任务相关信息（如转换领域）时，TSG会在统一模型中自动激活，在TIE提取的转换信息指导下生成转换句子来发起这次转换。实验结果表明，该主动转换方法取得了良好性能。通过利用条件随机场（CRF），我们在TIE模型上获得了显著的性能提升。TSG在保持正常闲聊和任务型回复生成的统一能力时，能够灵活生成转换句子。