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Audrey: A Personalized Open-Domain Conversational Bot (2011.05910v1)

Published 11 Nov 2020 in cs.CL and cs.AI

Abstract: 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.

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Authors (13)
  1. Chung Hoon Hong (2 papers)
  2. Yuan Liang (31 papers)
  3. Sagnik Sinha Roy (1 paper)
  4. Arushi Jain (13 papers)
  5. Vihang Agarwal (1 paper)
  6. Ryan Draves (1 paper)
  7. Zhizhuo Zhou (3 papers)
  8. William Chen (49 papers)
  9. Yujian Liu (15 papers)
  10. Martha Miracky (1 paper)
  11. Lily Ge (1 paper)
  12. Nikola Banovic (4 papers)
  13. David Jurgens (69 papers)
Citations (7)