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.

我们研究了从闲聊到任务相关服务的对话场景，并探讨了一个统一的对话模型如何在对话模式从闲聊切换到任务导向时以一种连贯和合作的方式主动参与。我们首先构建了一个过渡信息提取器（TIE），用于跟踪先前的闲聊互动并检测用户转向任务导向服务的潜在意图。同时，在统一模型中，通过高效的适配器调优和过渡提示学习，扩展了一个过渡句子生成器（TSG）。当 TIE 成功从先前的闲聊中找到与任务相关的信息，例如过渡领域时，TSG 会在统一模型中自动被激活，通过根据 TIE 提取的过渡信息生成一个过渡句子来启动这个转换。实验结果显示出有关主动转换的良好性能。我们通过使用条件随机场（CRF）在 TIE 模型上实现了额外的大幅改进。TSG 能够灵活地生成过渡句子，同时保持常规闲聊和任务导向响应生成的统一能力。