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This paper describes our participation in the DEtection of TOXicity incomments In Spanish (DETOXIS) shared task 2021 at the 3rd Workshop on IberianLanguages Evaluation Forum. The shared task is divided into two relatedclassification tasks: (i) Task 1: toxicity detection and; (ii) Task 2: toxicitylevel detection. They focus on the xenophobic problem exacerbated by the spreadof toxic comments posted in different online news articles related toimmigration. One of the necessary efforts towards mitigating this problem is todetect toxicity in the comments. Our main objective was to implement anaccurate model to detect xenophobia in comments about web news articles withinthe DETOXIS shared task 2021, based on the competition's official metrics: theF1-score for Task 1 and the Closeness Evaluation Metric (CEM) for Task 2. Tosolve the tasks, we worked with two types of machine learning models: (i)statistical models and (ii) Deep Bidirectional Transformers for LanguageUnderstanding (BERT) models. We obtained our best results in both tasks usingBETO, an BERT model trained on a big Spanish corpus. We obtained the 3rd placein Task 1 official ranking with the F1-score of 0.5996, and we achieved the 6thplace in Task 2 official ranking with the CEM of 0.7142. Our results suggest:(i) BERT models obtain better results than statistical models for toxicitydetection in text comments; (ii) Monolingual BERT models have an advantage overmultilingual BERT models in toxicity detection in text comments in theirpre-trained language.

AI-UPV at IberLEF-2021 DETOXIS task: Toxicity Detection in Immigration-Related Web News Comments Using Transformers and Statistical Models