RoBERTuito: a pre-trained language model for social media text in Spanish

TL;DR

RoBERTuito is a Spanish language model trained on 500 million tweets, outperforming existing models on user-generated text tasks and showing competitive results in code-switching benchmarks.

Contribution

This work introduces RoBERTuito, a domain-specific pre-trained language model for Spanish social media text, and demonstrates its superior performance on relevant NLP tasks.

Findings

Outperforms other Spanish language models on user-generated text tasks

Achieves top results on some English-Spanish code-switching benchmarks

Competitive performance against monolingual English models

Abstract

Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for Natural Language Understanding tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks. However, for languages other than English such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model achieves top results for some English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and has also competitive performance against monolingual models in English tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.