构建以用户为中心、内容驱动的社交机器人
Building A User-Centric and Content-Driven Socialbot
摘要：
为了构建Sounding Board，我们开发了一个能够容纳我们为社交机器人对话设计的对话策略的系统架构。该体系结构包括一个多维的语言理解模块，用于分析用户的话语，一个分层对话管理框架的对话上下文跟踪和复杂的对话控制，和一个语言生成过程，实现响应计划，并作出调整语音合成。此外，我们构建了一个新的知识库，通过从各种来源收集社交聊天内容来为社交机器人提供动力。该系统的一个重要贡献是知识库和对话管理之间的协同作用，即，使用图形结构来组织知识库，使得对话框控制在将相关内容引入讨论中时非常有效。我们利用比赛期间从Sounding Board收集的数据，对社交机器人对话和用户评分进行了深入分析，为社交机器人的评估方法提供了有价值的见解。我们还调查了一种新的方法，系统的评估和诊断，允许评分个别对话段的对话。最后，观察到socialbots遭受浅对话与非结构化数据相关的主题的问题，我们研究的问题，使扩展的socialbot对话的基础上的文件。为了将机器阅读和对话控制技术结合起来，提出了一种基于图形的文档表示方法，以及自动构建图形的方法。使用基于图形的表示，对话框控制可以通过检索节点或沿着图形中的边移动来执行。为了说明使用，混合主动对话策略被设计用于新闻文章上的社交机器人对话。
英文摘要：
To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations. The architecture consists of a multi-dimensional language understanding module for analyzing user utterances, a hierarchical dialog management framework for dialog context tracking and complex dialog control, and a language generation process that realizes the response plan and makes adjustments for speech synthesis. Additionally, we construct a new knowledge base to power the socialbot by collecting social chat content from a variety of sources. An important contribution of the system is the synergy between the knowledge base and the dialog management, i.e., the use of a graph structure to organize the knowledge base that makes dialog control very efficient in bringing related content to the discussion. Using the data collected from Sounding Board during the competition, we carry out in-depth analyses of socialbot conversations and user ratings which provide valuable insights in evaluation methods for socialbots. We additionally investigate a new approach for system evaluation and diagnosis that allows scoring individual dialog segments in the conversation. Finally, observing that socialbots suffer from the issue of shallow conversations about topics associated with unstructured data, we study the problem of enabling extended socialbot conversations grounded on a document. To bring together machine reading and dialog control techniques, a graph-based document representation is proposed, together with methods for automatically constructing the graph. Using the graph-based representation, dialog control can be carried out by retrieving nodes or moving along edges in the graph. To illustrate the usage, a mixed-initiative dialog strategy is designed for socialbot conversations on news articles.
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。
