Yichen Cai, Pei Chen, Jiayang Li, Jingya Guo, Zejian Li, Changyuan Yang, Lingyun Sun. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Yichen Cai, Jiayang Li, Junyuan Qiu, Jingya Guo, Weitao You, Changyuan Yang, Lingyun Sun, Pei Chen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Conversational Agents (CAs) can facilitate information elicitation in various scenarios, such as semi-structured interviews. Current CAs can ask predetermined questions but lack skills for asking follow-up questions. Thus, we designed three approaches for CAs to automatically ask follow-up questions, i.e., follow-ups on concepts, follow-ups on related concepts, and general follow-ups. To investigate their effects, we conducted a user study (N=26) in which a CA interviewer asked follow-up questions generated by algorithms and crafted by human wizards. Our results showed that the CA's follow-up questions were readable and effective in information elicitation. The follow-ups on concepts and related concepts achieved a lower drop rate and better relevance, while the general follow-ups elicited more informative responses. Further qualitative analysis of the human-CA interview data revealed algorithm drawbacks and identified follow-up question techniques used by the human wizards. We provided design implications for improving information elicitation of future CAs based on the results.

Alerting customers on suspected online-payment fraud and persuade them to terminate transactions is increasingly requested with the rapid growth of digital finance worldwide. We explored the feasibility of using a conversational agent (CA) to fulfill this request. Shing, a voice-based CA, proactively initializes and repairs the conversation with empathetical communication skills in order to alert customers when a suspected online-payment fraud is detected, collects important information for fraud scrutiny and persuades customers to terminate the transaction once the fraud is confirmed. We evaluated our system by comparing it with a rule-based CA with regards to customer response and perceptions in a real-world context where our systems took 144,795 phone calls in total in which 83,019 (57.3%) natural breakdowns happened. Results showed that more customers stopped risky transactions after conversing with Shing. They seemed more willing to converse with Shing for more dialogue turns and provide transaction details. Our work presents practical implications for the design of proactive CA.