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arXiv · 2202.03829

TimeLMs: Diachronic Language Models from Twitter

Daniel Loureiro·Francesco Barbieri·Leonardo Neves·Luis Espinosa Anke·Jose Camacho-Collados
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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.

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Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, Jose Camacho-Collados. 2022-04-01. TimeLMs: Diachronic Language Models from Twitter. https://arxiv.org/abs/2202.03829

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