论文标题
学习作为对话：对话系统加强了信息获取
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition
论文作者
论文摘要
我们提出了新颖的AI授权聊天机器人，以学习作为对话，其中用户没有阅读段落，而是通过与老师机器人的对话获得信息和知识。我们的以信息为导向的对话系统采用了增强自我播放的新颖适应性，因此可以将系统转移到无域对话数据的情况下转移到各个域，并可以对用户进行信息丰富和专心的对话。我们对三个大型公共数据语料库的广泛主观和客观评估表明，我们系统的有效性是提供知识密集和专注的对话，并帮助最终用户在不阅读段落的情况下获得知识。我们的代码和数据集可公开用于后续研究。
We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. Our information-acquisition-oriented dialogue system employs a novel adaptation of reinforced self-play so that the system can be transferred to various domains without in-domain dialogue data, and can carry out conversations both informative and attentive to users. Our extensive subjective and objective evaluations on three large public data corpora demonstrate the effectiveness of our system to deliver knowledge-intensive and attentive conversations and help end users substantially gain knowledge without reading passages. Our code and datasets are publicly available for follow-up research.
