  Language models are ubiquitous in current NLP, and their multilingual
capacity has recently attracted considerable attention. However, current
analyses have almost exclusively focused on (multilingual variants of) standard
benchmarks, and have relied on clean pre-training and task-specific corpora as
multilingual signals. In this paper, we introduce XLM-T, a framework for using
and evaluating multilingual language models in Twitter. This framework features
two main assets: (1) a strong multilingual baseline consisting of an XLM-R
(Conneau et al. 2020) model pre-trained on millions of tweets in over thirty
languages, alongside starter code to subsequently fine-tune on a target task;
and (2) a set of unified sentiment analysis Twitter datasets in eight different
languages. This is a modular framework that can easily be extended to
additional tasks, as well as integrated with recent efforts also aimed at the
homogenization of Twitter-specific datasets (Barbieri et al. 2020).
