Abstract
Dialog participants sometimes align their linguistic styles, e.g., they use the same words and syntactic constructions as their interlocutors. We propose to investigate the notion of lexico-semantic alignment: to what extent do speakers convey the same meaning when they use the same words? We design measures of lexico-semantic alignment relying on contextualized word representations. We show that they reflect interesting semantic differences between the two sides of a debate and that they can assist in the task of debate’s winner prediction.- Anthology ID:
- 2023.sicon-1.6
- Volume:
- Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Kushal Chawla, Weiyan Shi
- Venue:
- SICon
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 50–63
- Language:
- URL:
- https://aclanthology.org/2023.sicon-1.6
- DOI:
- 10.18653/v1/2023.sicon-1.6
- Cite (ACL):
- Aina Garí Soler, Matthieu Labeau, and Chloé Clavel. 2023. Measuring Lexico-Semantic Alignment in Debates with Contextualized Word Representations. In Proceedings of the First Workshop on Social Influence in Conversations (SICon 2023), pages 50–63, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Measuring Lexico-Semantic Alignment in Debates with Contextualized Word Representations (Garí Soler et al., SICon 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.sicon-1.6.pdf