Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts

• URL: http://arxiv.org/abs/2101.06004v1
• Date: Fri, 15 Jan 2021 07:49:27 GMT
• Title: Walk in Wild: An Ensemble Approach for Hostility Detection in Hindi Posts
• Authors: Chander Shekhar, Bhavya Bagla, Kaushal Kumar Maurya, Maunendra Sankar Desarkar
• Abstract summary: We develop a simple ensemble based model on pre-trained mBERT and popular classification algorithms like Artificial Neural Network (ANN) and XGBoost for hostility detection in Hindi posts. We received third overall rank in the competition and weighted F1-scores of 0.969 and 0.61 on the binary and multi-label multi-class classification tasks respectively.
• Score: 3.9373541926236766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the reach of the internet increases, pejorative terms started flooding over social media platforms. This leads to the necessity of identifying hostile content on social media platforms. Identification of hostile contents on low-resource languages like Hindi poses different challenges due to its diverse syntactic structure compared to English. In this paper, we develop a simple ensemble based model on pre-trained mBERT and popular classification algorithms like Artificial Neural Network (ANN) and XGBoost for hostility detection in Hindi posts. We formulated this problem as binary classification (hostile and non-hostile class) and multi-label multi-class classification problem (for more fine-grained hostile classes). We received third overall rank in the competition and weighted F1-scores of ~0.969 and ~0.61 on the binary and multi-label multi-class classification tasks respectively.

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