Chun-Yen Chen

Also published as: Chun Yen Chen


2023

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ChatBack: Investigating Methods of Providing Grammatical Error Feedback in a GUI-based Language Learning Chatbot
Kai-Hui Liang | Sam Davidson | Xun Yuan | Shehan Panditharatne | Chun-Yen Chen | Ryan Shea | Derek Pham | Yinghua Tan | Erik Voss | Luke Fryer
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

The increasing use of AI chatbots as conversation partners for second-language learners highlights the importance of providing effective feedback. To ensure a successful learning experience, it is essential for researchers and practitioners to understand the optimal timing, methods of delivery, and types of feedback that are most beneficial to learners. Synchronous grammar corrective feedback (CF) has been shown to be more effective than asynchronous methods in online writing tasks. Additionally, self-correction by language learners has proven more beneficial than teacher-provided correction, particularly for spoken language skills and non-novice learners. However, existing language-learning AI chatbots often lack synchronous CF and self-correction capabilities. To address this, we propose a synchronous conversational corrective feedback (CCF) method, which allows self-correction and provides metalinguistic explanations (ME). Our study suggests that in chatbot-driven language-learning tools, corrective feedback is more effectively delivered through means other than the social chatbot, such as a GUI interface. Furthermore, we found that guided self-correction offers a superior learning experience compared to providing explicit corrections, particularly for learners with high learning motivation or lower linguistic ability.

2019

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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.

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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.