响应生成中按需知识集成的响应预期记忆
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation
摘要：
神经会话模型通常会生成适当但不提供信息的响应。可以显著增强信息量的场景是阅读对话（CbR），其中对话发生在给定的外部文档上。在以前的工作中，外部文档是通过（1）创建一个上下文感知的文档存储器，它集成了来自文档和会话上下文的信息，然后（2）生成引用存储器的响应。在本文中，我们建议创建的文件记忆与一些预期的反应。这是通过师生框架实现的。教师被给予外部文档、上下文和地面真实响应，并学习如何从三个信息源建立响应感知文档记忆。学生学习从前两个来源和教师对记忆创造的洞察力来构建响应预期的文档记忆。实证结果表明，我们的模型优于以前的国家的最先进的CbR任务。
英文摘要：
Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document. In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory. In this paper, we propose to create the document memory with some anticipated responses in mind. This is achieved using a teacher-student framework. The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information. The student learns to construct a response-anticipated document memory from the first two sources, and the teacher's insight on memory creation. Empirical results show that our model outperforms the previous state-of-the-art for the CbR task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
