TimeLMs: Diachronic Language Models from Twitter

Understand

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.