Papers › XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond
Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados
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 model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new 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 and a XLM-T model fine-tuned on them.
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Code
Syntology Ran 3 of 3 code samples harvested from 1 repository linked to this paper; 0 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · our draft was wrong.
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Code Syntology ran Syntology
3 samples harvested; 3 ran; 1 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | TweetEval | RoB-RT | ALL | 65.2 | #2 of 7 | Archive leaderboard | report |
| Sentiment Analysis | TweetEval | RoB-RT | Emoji | 31.4 | #2 of 7 | Archive leaderboard | report |
| Sentiment Analysis | TweetEval | RoB-RT | Emotion | 79.5 | #2 of 7 | Archive leaderboard | report |
| Sentiment Analysis | TweetEval | RoB-RT | Hate | 52.3 | #2 of 7 | Archive leaderboard | report |
| Sentiment Analysis | TweetEval | RoB-RT | Irony | 61.7 | #2 of 7 | Archive leaderboard | report |
| Sentiment Analysis | TweetEval | RoB-RT | Offensive | 80.5 | #2 of 7 | Archive leaderboard | report |
| Sentiment Analysis | TweetEval | RoB-RT | Sentiment | 72.6 | #2 of 7 | Archive leaderboard | report |
| Sentiment Analysis | TweetEval | RoB-RT | Stance | 69.3 | #2 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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