TimeLMs: Diachronic Language Models from Twitter

• URL: http://arxiv.org/abs/2202.03829v1
• Date: Tue, 8 Feb 2022 12:47:38 GMT
• Title: TimeLMs: Diachronic Language Models from Twitter
• Authors: Daniel Loureiro, Francesco Barbieri, Leonardo Neves, Luis Espinosa Anke, Jose Camacho-Collados
• Abstract summary: 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.
• Score: 22.166929118357714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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