TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media

Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados


Abstract
Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.
Anthology ID:
2022.coling-1.296
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:
3353–3359
Language:
URL:
https://aclanthology.org/2022.coling-1.296
DOI:
Bibkey:
Cite (ACL):
Daniel Loureiro, Aminette D’Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa-Anke, Leonardo Neves, Francesco Barbieri, and Jose Camacho-Collados. 2022. TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3353–3359, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media (Loureiro et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.296.pdf
Code
 cardiffnlp/tempowic
Data
WiC