Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related to the topic. In this work, we contribute a novel modular dialogue system framework that seamlessly integrates factual information and social content into persuasive dialogue. Our framework is generalizable to any dialogue tasks that have mixed social and task contents. We conducted a study that compared user evaluations of our framework versus a baseline end-to-end generation model. We found our framework was evaluated more favorably in all dimensions including competence and friendliness, compared to the end-to-end model which does not explicitly handle social content or factual questions.

翻译：复杂的对话环境,例如说服涉及交流态度或行为的变化,因此,用户的观点需要讨论,即使与本专题没有直接关系。在这项工作中,我们贡献了一个新型模块对话系统框架,将事实信息和社会内容无缝地纳入有说服力的对话。我们的框架可以概括到具有混合社会和任务内容的对话任务中。我们进行了一项研究,将我们框架的用户评价与基线端到端的生成模式进行比较。我们发现,与没有明确处理社会内容或事实问题的端到端模式相比,我们的框架在所有层面都得到了更有利的评价,包括能力和友好性。