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Offensive Language Identification in Transliterated and Code-Mixed Bangla

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arxiv 2311.15023 v1 pith:IYCVNVZL submitted 2023-11-25 cs.CL

Md Nishat Raihan , Umma Hani Tanmoy , Anika Binte Islam , Kai North , Tharindu Ranasinghe , Antonios Anastasopoulos , Marcos Zampieri This is my paper
classification cs.CL
keywords offensivedatasetlanguagebanglacreatingidentificationmodelsresults
verification ladder T0 review T1 audit T2 compute T3 formal
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Identifying offensive content in social media is vital for creating safe online communities. Several recent studies have addressed this problem by creating datasets for various languages. In this paper, we explore offensive language identification in texts with transliterations and code-mixing, linguistic phenomena common in multilingual societies, and a known challenge for NLP systems. We introduce TB-OLID, a transliterated Bangla offensive language dataset containing 5,000 manually annotated comments. We train and fine-tune machine learning models on TB-OLID, and we evaluate their results on this dataset. Our results show that English pre-trained transformer-based models, such as fBERT and HateBERT achieve the best performance on this dataset.

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  1. Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models?

    cs.CL 2025-06 conditional novelty 5.0 of 10

    On three hate speech datasets, including a new code-mixed IndoHateMix benchmark, fine-tuned LLMs such as LLaMA-3.1 beat multilingual BERT models, with the largest gains on code-mixed Indian text.

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