One Word, Two Sides: Traces of Stance in Contextualized Word Representations

Aina Garí Soler, Matthieu Labeau, Chloé Clavel


Abstract
The way we use words is influenced by our opinion. We investigate whether this is reflected in contextualized word embeddings. For example, is the representation of “animal” different between people who would abolish zoos and those who would not? We explore this question from a Lexical Semantic Change standpoint. Our experiments with BERT embeddings derived from datasets with stance annotations reveal small but significant differences in word representations between opposing stances.
Anthology ID:
2022.coling-1.347
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3950–3959
Language:
URL:
https://aclanthology.org/2022.coling-1.347
DOI:
Bibkey:
Cite (ACL):
Aina Garí Soler, Matthieu Labeau, and Chloé Clavel. 2022. One Word, Two Sides: Traces of Stance in Contextualized Word Representations. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3950–3959, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
One Word, Two Sides: Traces of Stance in Contextualized Word Representations (Garí Soler et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.347.pdf
Code
 ainagari/1word2sides