Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates
Suzanna Sia, Kokil Jaidka, Hansin Ahuja, Niyati Chhaya, Kevin Duh
Abstract
In contexts where debate and deliberation are the norm, the participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit Change My View and Intelligence Squared debates datasets. We adopt a hierarchical generative Variational Autoencoder as our model and impose structural constraints that reflect competing hypotheses about the nature of argumentation. Our findings suggest that in most settings, predictive models that anticipate winning arguments to be further from the initial argument of the opinion holder are more likely to succeed.- Anthology ID:
- 2022.emnlp-main.818
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11939–11950
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.818
- DOI:
- 10.18653/v1/2022.emnlp-main.818
- Cite (ACL):
- Suzanna Sia, Kokil Jaidka, Hansin Ahuja, Niyati Chhaya, and Kevin Duh. 2022. Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11939–11950, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (Sia et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.emnlp-main.818.pdf