ANDES at SemEval-2020 Task 12: A Jointly-trained BERT Multilingual Model for Offensive Language Detection

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1524–1531 Barcelona, Spain (Online), December 12, 2020. 1524 ANDES at SemEval-2020 Task 12: A jointly-trained BERT multilingual model for offensive language detection Juan Manuel P´erez∗ ICC, CONICET Universidad de Buenos Aires jmperez@dc.uba.ar Aym´e Arango∗ University of Chile IMFD, Chile aarango@dcc.uchile.cl Franco M. Luque CONICET Universidad Nacional de C´ordoba francolq@famaf.unc.edu.ar Abstract This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research1. 1 Introduction Offensive language, hate speech, cyberbullying, and abusive language detection are topics that have summoned a lot of interest in the last years, particularly due to the necessity in Social Media to stop –or at least, to diminish– this omnipresent phenomenon. Not only the social implications are behind this, but also practical implications for companies: recently, some large advertisers have removed their presence from Social Media as they consider that platforms full of “divisiveness and hate speech” do not give value to their companies (Hern, 2020). The differences between these categories are loosely defined. Waseem et al. (2017) propose using two axes to understand the particularities of each discourse: whether the abusive language is directed to a specific individual or to a generalized group, and whether the offending discourse is explicit or implicit. Even when online platforms prohibit behavior that crosses the line into abuse,2 these rules are frequently violated. Automated moderation algorithms are necessary to perform a faster and even better user- generated-content moderation or to serve as a tool that helps human moderators to reduce the volume of offensive content still present in online platforms. To address this problem, offensive language detection is usually thought of as a binary classification problem in which the input is a post (tweet, message, comment, etc.) and the output is the classification of whether the post is offensive or not. Several features of offensive language make it complex to detect. The offenders could intentionally hide offensive words by substituting letters with special characters or numbers. Content with irony or sarcasm could be harmful even when all words are polite, and vice versa. The real intention behind speech is sometimes difficult to detect even for humans. According to Chatzakou et al. (2017), in the case of Twitter, each tweet provides a fairly limited context. Therefore, an offensive post may be identified as unoffensive if the context is not taken into account. Despite several published proposals on detecting offensive language, hate speech, and other related concepts, there is no consolidated or effective strategy that could be applied to different languages and domains. ∗Authors contributed equally 1https://github.com/finiteautomata/offenseval2020 This work is licensed …

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