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    <abstract>Hate speech occurs more often than ever and polarizes society. To help counter this polarization, SemEval 2019 organizes a shared task called the Multilingual Detection of Hate. The first task (A) is to decide whether a given tweet contains hate against immigrants or women, in a multilingual perspective, for English and Spanish. In the second task (B), the system is also asked to classify the following sub-tasks: hateful tweets as aggressive or not aggressive, and to identify the target harassed as individual or generic. We evaluate multiple models, and finally combine them in an ensemble setting. This ensemble setting is built of five and three submodels for the English and Spanish task respectively. In the current setup it shows that using a bigger ensemble for English tweets performs mediocre, while a slightly smaller ensemble does work well for detecting hate speech in Spanish tweets. Our results on the test set for English show 0.378 macro F1 on task A and 0.553 macro F1 on task B. For Spanish the results are significantly higher, 0.701 macro F1 on task A and 0.734 macro F1 for task B.</abstract>
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%0 Conference Proceedings
%T Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate
%A Zhang, Mike
%A David, Roy
%A Graumans, Leon
%A Timmerman, Gerben
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F zhang-etal-2019-grunn2019
%X Hate speech occurs more often than ever and polarizes society. To help counter this polarization, SemEval 2019 organizes a shared task called the Multilingual Detection of Hate. The first task (A) is to decide whether a given tweet contains hate against immigrants or women, in a multilingual perspective, for English and Spanish. In the second task (B), the system is also asked to classify the following sub-tasks: hateful tweets as aggressive or not aggressive, and to identify the target harassed as individual or generic. We evaluate multiple models, and finally combine them in an ensemble setting. This ensemble setting is built of five and three submodels for the English and Spanish task respectively. In the current setup it shows that using a bigger ensemble for English tweets performs mediocre, while a slightly smaller ensemble does work well for detecting hate speech in Spanish tweets. Our results on the test set for English show 0.378 macro F1 on task A and 0.553 macro F1 on task B. For Spanish the results are significantly higher, 0.701 macro F1 on task A and 0.734 macro F1 for task B.
%R 10.18653/v1/S19-2069
%U https://aclanthology.org/S19-2069
%U https://doi.org/10.18653/v1/S19-2069
%P 391-395

Markdown (Informal)

[Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate](https://aclanthology.org/S19-2069) (Zhang et al., SemEval 2019)

• Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate (Zhang et al., SemEval 2019)

ACL

• Mike Zhang, Roy David, Leon Graumans, and Gerben Timmerman. 2019. Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 391–395, Minneapolis, Minnesota, USA. Association for Computational Linguistics.