Transformers and Ensemble methods: A solution for Hate Speech Detection in Arabic languages
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866915398621855744 |
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| author | de Paula, Angel Felipe Magnossão Bensalem, Imene Rosso, Paolo Zaghouani, Wajdi |
| author_facet | de Paula, Angel Felipe Magnossão Bensalem, Imene Rosso, Paolo Zaghouani, Wajdi |
| contents | This paper describes our participation in the shared task of hate speech detection, which is one of the subtasks of the CERIST NLP Challenge 2022. Our experiments evaluate the performance of six transformer models and their combination using 2 ensemble approaches. The best results on the training set, in a five-fold cross validation scenario, were obtained by using the ensemble approach based on the majority vote. The evaluation of this approach on the test set resulted in an F1-score of 0.60 and an Accuracy of 0.86. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_09823 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Transformers and Ensemble methods: A solution for Hate Speech Detection in Arabic languages de Paula, Angel Felipe Magnossão Bensalem, Imene Rosso, Paolo Zaghouani, Wajdi Computation and Language Artificial Intelligence Machine Learning This paper describes our participation in the shared task of hate speech detection, which is one of the subtasks of the CERIST NLP Challenge 2022. Our experiments evaluate the performance of six transformer models and their combination using 2 ensemble approaches. The best results on the training set, in a five-fold cross validation scenario, were obtained by using the ensemble approach based on the majority vote. The evaluation of this approach on the test set resulted in an F1-score of 0.60 and an Accuracy of 0.86. |
| title | Transformers and Ensemble methods: A solution for Hate Speech Detection in Arabic languages |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2303.09823 |