CBR for interpretable response selection in conversational modelling.

Suresh, Malavika

Abstract

Current state-of-the-art dialogue systems are increasingly complex. When used in applications such as motivational interviewing, the lack of interpretability is a concern. CBR offers to bridge this gap by using the most similar past cases to decide the outcome for a new problem, which then serves as a natural as well as accurate explanation of the outcome. This research proposes to extend the Abstract Argumentation CBR (AA-CBR) framework for predicting the next response type in an ongoing conversation by reusing the knowledge of previous conversations to achieve a desirable outcome for a new conversation context.

Citation

SURESH, M. 2022. CBR for interpretable response selection in conversational modelling. In Reuss, P. and Schönborn, J. (eds.) Proceedings of the 30th Doctoral consortium of the international conference on case-based reasoning (ICCBR-DC 2022), co-located with the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. CEUR workshop proceedings, 3418. Aachen: CEUR-WS [online], pages 28-33. Available from: https://ceur-ws.org/Vol-3418/ICCBR_2022_DC_paper25.pdf

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 30th Doctoral consortium of the international conference on case-based reasoning (ICCBR-DC 2022), co-located with the 30th International conference on case-based reasoning (ICCBR 2022)
• Start Date: Jul 12, 2022
• End Date: Jul 15, 2022
• Acceptance Date: Jul 22, 2022
• Online Publication Date: Sep 15, 2022
• Publication Date: Jun 13, 2023
• Deposit Date: Jul 21, 2023
• Publicly Available Date: Jul 21, 2023
• Publisher: CEUR-WS
• Peer Reviewed: Peer Reviewed
• Pages: 28-33
• Series Title: CEUR workshop proceedings
• Series Number: 3418
• Series ISSN: 1613-0073
• Keywords: Case based reasoning; Conversational modelling; Motivational interviewing; Abstract argumentation
• Public URL: https://rgu-repository.worktribe.com/output/2015578
• Publisher URL: https://ceur-ws.org/Vol-3418/ICCBR_2022_DC_paper25.pdf