Chun-Yen Chen

Also published as: Chun Yen Chen


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A Large-Scale User Study of an Alexa Prize Chatbot: Effect of TTS Dynamism on Perceived Quality of Social Dialog
Michelle Cohn | Chun-Yen Chen | Zhou Yu
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

This study tests the effect of cognitive-emotional expression in an Alexa text-to-speech (TTS) voice on users’ experience with a social dialog system. We systematically introduced emotionally expressive interjections (e.g., “Wow!”) and filler words (e.g., “um”, “mhmm”) in an Amazon Alexa Prize socialbot, Gunrock. We tested whether these TTS manipulations improved users’ ratings of their conversation across thousands of real user interactions (n=5,527). Results showed that interjections and fillers each improved users’ holistic ratings, an improvement that further increased if the system used both manipulations. A separate perception experiment corroborated the findings from the user study, with improved social ratings for conversations including interjections; however, no positive effect was observed for fillers, suggesting that the role of the rater in the conversation—as active participant or external listener—is an important factor in assessing social dialogs.

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Gunrock: A Social Bot for Complex and Engaging Long Conversations
Dian Yu | Michelle Cohn | Yi Mang Yang | Chun Yen Chen | Weiming Wen | Jiaping Zhang | Mingyang Zhou | Kevin Jesse | Austin Chau | Antara Bhowmick | Shreenath Iyer | Giritheja Sreenivasulu | Sam Davidson | Ashwin Bhandare | Zhou Yu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists. We focus on understanding complex sentences and having in-depth conversations in open domains. In this paper, we introduce some innovative system designs and related validation analysis. Overall, we found that users produce longer sentences to Gunrock, which are directly related to users’ engagement (e.g., ratings, number of turns). Additionally, users’ backstory queries about Gunrock are positively correlated to user satisfaction. Finally, we found dialog flows that interleave facts and personal opinions and stories lead to better user satisfaction.