Abstract
People leverage group discussions to collaborate in order to solve complex tasks, e.g. in project meetings or hiring panels. By doing so, they engage in a variety of conversational strategies where they try to convince each other of the best approach and ultimately reach a decision. In this work, we investigate methods for detecting what makes someone change their mind. To this end, we leverage a recently introduced dataset containing group discussions of people collaborating to solve a task. To find out what makes someone change their mind, we incorporate various techniques such as neural text classification and language-agnostic change point detection. Evaluation of these methods shows that while the task is not trivial, the best way to approach it is using a language-aware model with learning-to-rank training. Finally, we examine the cues that the models develop as indicative of the cause of a change of mind.- Anthology ID:
- 2022.sigdial-1.52
- Volume:
- Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- September
- Year:
- 2022
- Address:
- Edinburgh, UK
- Editors:
- Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 552–563
- Language:
- URL:
- https://aclanthology.org/2022.sigdial-1.52
- DOI:
- 10.18653/v1/2022.sigdial-1.52
- Cite (ACL):
- Georgi Karadzhov, Tom Stafford, and Andreas Vlachos. 2022. What makes you change your mind? An empirical investigation in online group decision-making conversations. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 552–563, Edinburgh, UK. Association for Computational Linguistics.
- Cite (Informal):
- What makes you change your mind? An empirical investigation in online group decision-making conversations (Karadzhov et al., SIGDIAL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.sigdial-1.52.pdf
- Data
- DeliData