TimeLMs: Diachronic Language Models from Twitter
Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, Jose Camacho-collados
Abstract
Despite its importance, the time variable has been largely neglected in the NLP and language model literature. In this paper, we present TimeLMs, a set of language models specialized on diachronic Twitter data. We show that a continual learning strategy contributes to enhancing Twitter-based language models’ capacity to deal with future and out-of-distribution tweets, while making them competitive with standardized and more monolithic benchmarks. We also perform a number of qualitative analyses showing how they cope with trends and peaks in activity involving specific named entities or concept drift. TimeLMs is available at github.com/cardiffnlp/timelms.- Anthology ID:
- 2022.acl-demo.25
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Valerio Basile, Zornitsa Kozareva, Sanja Stajner
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 251–260
- Language:
- URL:
- https://aclanthology.org/2022.acl-demo.25
- DOI:
- 10.18653/v1/2022.acl-demo.25
- Cite (ACL):
- Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, and Jose Camacho-collados. 2022. TimeLMs: Diachronic Language Models from Twitter. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 251–260, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- TimeLMs: Diachronic Language Models from Twitter (Loureiro et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-demo.25.pdf
- Code
- cardiffnlp/timelms + additional community code
- Data
- TweetEval