We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark progress, we introduce a new dataset of GuessMovie, which contains 16,881 documents, each describing a movie, and associated 13,434 dialogues. Further, we propose the MD3 model. Keeping guessing the target document in mind, it converses with the user conditioned on both document engagement and user feedback. In order to incorporate large-scale external documents into the dialogue, it pretrains a document representation which is sensitive to attributes it talks about an object. Then it tracks dialogue state by detecting evolvement of document belief and attribute belief, and finally optimizes dialogue policy in principle of entropy decreasing and reward increasing, which is expected to successfully guess the user's target in a minimum number of turns. Experiments show that our method significantly outperforms several strong baseline methods and is very close to human's performance.

翻译：我们提出了一个新的任务,即多文档驱动器对话(MD3),其中代理商可以猜测用户通过引导对话感兴趣的目标文件。为了衡量进展,我们引入了GuessMovie的新数据集,该数据集包含16,881份文件,每个文件描述一部电影,以及相关的13,434个对话。此外,我们提议了MD3模式。在猜测目标文件时,它与用户的对立以文件参与和用户反馈为条件。为了将大型外部文件纳入对话,它预设了一个文件代表,该代表对于让用户谈论某个对象具有敏感性。然后,它通过检测文件信仰和属性信仰的演变来跟踪对话状态,最后优化了在加密减少和增加报酬原则上的对话政策,预计这将在最小的翻转数中成功猜测用户的目标。实验显示,我们的方法大大超越了几个强有力的基线方法,并且非常接近人类的性能。