Abstract
Secretary-General António Guterres launched the United Nations Strategy and Plan of Action on Hate Speech in 2019, recognizing the alarming trend of increasing hate speech worldwide. Despite extensive research, benchmark datasets for hate speech detection remain limited in volume and vary in domain and annotation. In this paper, the following research objectives are deliberated (a) performance comparisons between multi-task models against single-task models; (b) performance study of different multi-task models (fully shared, shared-private) for hate speech detection, considering individual dataset as a separate task; (c) what is the effect of using different combinations of available existing datasets in the performance of multi-task settings? A total of six datasets that contain offensive and hate speech on the accounts of race, sex, and religion are considered for the above study. Our analysis suggests that a proper combination of datasets in a multi-task setting can overcome data scarcity and develop a unified framework.
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The Authors would like to acknowledge the support of Ministry of Home Affairs (MHA), India, for conducting this research.
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Maity, K., Balaji, G., Saha, S. (2024). Towards Analyzing the Efficacy of Multi-task Learning in Hate Speech Detection. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14452. Springer, Singapore. https://doi.org/10.1007/978-981-99-8076-5_23
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