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        <title>Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection</title>
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    <name type="personal">
        <namePart type="given">Prashant</namePart>
        <namePart type="family">Kapil</namePart>
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            <title>Proceedings of the 17th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
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            <namePart type="given">Misra</namePart>
            <namePart type="family">Sharma</namePart>
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        <name type="personal">
            <namePart type="given">Rajeev</namePart>
            <namePart type="family">Sangal</namePart>
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    <abstract>With the exponential rise in user-generated web content on social media, the proliferation of abusive languages towards an individual or a group across the different sections of the internet is also rapidly increasing. It is very challenging for human moderators to identify the offensive contents and filter those out. Deep neural networks have shown promise with reasonable accuracy for hate speech detection and allied applications. However, the classifiers are heavily dependent on the size and quality of the training data. Such a high-quality large data set is not easy to obtain. Moreover, the existing data sets that have emerged in recent times are not created following the same annotation guidelines and are often concerned with different types and sub-types related to hate. To solve this data sparsity problem, and to obtain more global representative features, we propose a Convolution Neural Network (CNN) based multi-task learning models (MTLs) to leverage information from multiple sources. Empirical analysis performed on three benchmark datasets shows the efficacy of the proposed approach with the significant improvement in accuracy and F-score to obtain state-of-the-art performance with respect to the existing systems.</abstract>
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            <start>491</start>
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%0 Conference Proceedings
%T Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection
%A Kapil, Prashant
%A Ekbal, Asif
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F kapil-ekbal-2020-leveraging
%X With the exponential rise in user-generated web content on social media, the proliferation of abusive languages towards an individual or a group across the different sections of the internet is also rapidly increasing. It is very challenging for human moderators to identify the offensive contents and filter those out. Deep neural networks have shown promise with reasonable accuracy for hate speech detection and allied applications. However, the classifiers are heavily dependent on the size and quality of the training data. Such a high-quality large data set is not easy to obtain. Moreover, the existing data sets that have emerged in recent times are not created following the same annotation guidelines and are often concerned with different types and sub-types related to hate. To solve this data sparsity problem, and to obtain more global representative features, we propose a Convolution Neural Network (CNN) based multi-task learning models (MTLs) to leverage information from multiple sources. Empirical analysis performed on three benchmark datasets shows the efficacy of the proposed approach with the significant improvement in accuracy and F-score to obtain state-of-the-art performance with respect to the existing systems.
%U https://aclanthology.org/2020.icon-main.66
%P 491-500

Markdown (Informal)

[Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection](https://aclanthology.org/2020.icon-main.66) (Kapil & Ekbal, ICON 2020)

• Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection (Kapil & Ekbal, ICON 2020)

ACL

• Prashant Kapil and Asif Ekbal. 2020. Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 491–500, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).