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Title
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OSACT4 shared tasks : ensembled stacked classification for offensive and hate speech in Arabic tweets
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Author
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Abstract
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| In this paper, we describe our submission for the OCAST4 2020 shared tasks on offensive language and hate speech detection in the Arabic language. Our solution builds upon combining a number of deep learning models using pre-trained word vectors. To improve the word representation and increase word coverage, we compare a number of existing pre-trained word embeddings and finally concatenate the two empirically best among them. To avoid under- as well as over-fitting, we train each deep model multiple times, and we include the optimization of the decision threshold into the training process. The predictions of the resulting models are then combined into a tuned ensemble by stacking a classifier on top of the predictions by these base models. We name our approach “ESOTP” (Ensembled Stacking classifier over Optimized Thresholded Predictions of multiple deep models). The resulting ESOTP-based system ranked 6th out of 35 on the shared task of Offensive Language detection (sub-task A) and 5th out of 30 on Hate Speech Detection (sub-task B). |
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Language
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English
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Source (book)
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4th Workshop on Open-Source Arabic Corpora and Processing Tools
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Publication
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European Language Resource Association
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2020
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Volume/pages
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(2020)
, p. 71-75
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Note
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Language Resources and Evaluation Conference (LREC 2020), Marseille, 11–16 May 2020
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Full text (open access)
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