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.
@article{arxiv.2011.05910,
title = {Audrey: A Personalized Open-Domain Conversational Bot},
author = {Chung Hoon Hong and Yuan Liang and Sagnik Sinha Roy and Arushi Jain and Vihang Agarwal and Ryan Draves and Zhizhuo Zhou and William Chen and Yujian Liu and Martha Miracky and Lily Ge and Nikola Banovic and David Jurgens},
journal= {arXiv preprint arXiv:2011.05910},
year = {2020}
}