论文标题
奥黛丽：个性化的开放域对话机器人
Audrey: A Personalized Open-Domain Conversational Bot
论文作者
论文摘要
会话智能要求一个人参与信息，个人和关系层面。自然语言理解的进步帮助最近的聊天机器人在信息级别的对话中取得了成功。但是，当前的技术仍然滞后在个人层面上与人交流并与他们完全相关的技术。密歇根大学向Alexa奖大挑战3 Audrey提交的是开放式的对话聊天机器人，旨在通过客户的个性和情感为指导的兴趣驱动的对话来吸引客户就这些级别吸引客户。奥黛丽（Audrey）建立在社会意识的模型中，例如情感检测和个人理解模块，以更深入地了解用户的兴趣和欲望。我们的体系结构使用与客户进行的混合方法在知识驱动的响应发生器和上下文驱动的神经响应发生器之间进行平衡，以迎合所有三个级别的对话。在半决赛期间，我们以1-5的李克特量表达到了平均累积评分为3.25。
Conversational Intelligence requires that a person engage on informational, personal and relational levels. Advances in Natural Language Understanding have helped recent chatbots succeed at dialog on the informational level. However, current techniques still lag for conversing with humans on a personal level and fully relating to them. The University of Michigan's submission to the Alexa Prize Grand Challenge 3, Audrey, is an open-domain conversational chat-bot that aims to engage customers on these levels through interest driven conversations guided by customers' personalities and emotions. Audrey is built from socially-aware models such as Emotion Detection and a Personal Understanding Module to grasp a deeper understanding of users' interests and desires. Our architecture interacts with customers using a hybrid approach balanced between knowledge-driven response generators and context-driven neural response generators to cater to all three levels of conversations. During the semi-finals period, we achieved an average cumulative rating of 3.25 on a 1-5 Likert scale.
