Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 64–69 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 64 Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification Juan Manuel P´erez Universidad de Buenos Aires CONICET jmperez@dc.uba.ar Franco M. Luque Universidad Nacional de C´ordoba CONICET francolq@famaf.unc.edu.ar Abstract In this article, we describe our participa- tion in HatEval, a shared task aimed at the detection of hate speech against immigrants and women. We focused on Spanish sub- tasks, building from our previous experiences on sentiment analysis in this language. We trained linear classifiers and Recurrent Neural Networks, using classic features, such as bag- of-words, bag-of-characters, and word embed- dings, and also with recent techniques such as contextualized word representations. In partic- ular, we trained robust task-oriented subword- aware embeddings and computed tweet rep- resentations using a weighted-averaging strat- egy. In the final evaluation, our systems showed competitive results for both Spanish subtasks ES-A and ES-B, achieving the first and fourth places respectively. 1 Introduction Hate speech against women, immigrants, and many other groups is a pervasive phenomenon on the Internet. On the early days of the World Wide Web, many academics adventured that prejudices and hatred would be removed in this space by the dissolution of identities (L´evy, 2001; Rheingold, 1993). Twenty years after this hypothesis, we can say that it has not been the case. The prevalence of racism in the “World White Web” has been stud- ied in a number of works (Adams and Roscigno, 2005; Kettrey and Laster, 2014) and so has been the misogyny in the virtual world (Filipovic, 2007; Mantilla, 2013). Racist and sexist discourse are a constant in so- cial media, but peaks are documented after “trig- ger” events, such as murders with religious or po- litical reasons (Burnap and Williams, 2015). Most social media companies are concerned about this issue and take actions against it; nonetheless, most of the efforts still need human intervention, mak- ing this task very expensive. Therefore, reducing human intervention is vital in order to have effec- tive tools to avoid the escalation of hate speech. HatEval (Basile et al., 2019) is a SemEval-2019 shared task aimed at the detection of hate speech towards immigrants and women in tweets. It com- prises two subtasks, with datasets in English (EN) and Spanish (ES) for both of them, giving a total of four subtasks. Subtask A is the binary classifica- tion of tweets into hateful or not hateful (HS). Sub- task B is a triple binary classification task where, in addition to HS, tweets are classified into aggres- sive or not aggressive (AG), and targets of hate speech are classified into single humans or groups of persons (TR). In this article, we present our participation in HatEval as team Atalaya. We focused our efforts on subtask A for Spanish (ES-A) but also worked at subtask B in Spanish (ES-B) and subtask A in English (EN-A). Our systems are based on our participation in the polarity …

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