OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification

• URL: http://arxiv.org/abs/2310.18387v2
• Date: Sat, 25 Nov 2023 13:13:01 GMT
• Title: OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification
• Authors: Dhiman Goswami, Md Nishat Raihan, Antara Mahmud, Antonios Anastasopoulos, Marcos Zampieri
• Abstract summary: Code-mixing is a well-studied linguistic phenomenon when two or more languages are mixed in text or speech. We introduce OffMix-3L, a novel offensive language identification dataset containing code-mixed data from three different languages.
• Score: 26.11758147703999
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Code-mixing is a well-studied linguistic phenomenon when two or more languages are mixed in text or speech. Several works have been conducted on building datasets and performing downstream NLP tasks on code-mixed data. Although it is not uncommon to observe code-mixing of three or more languages, most available datasets in this domain contain code-mixed data from only two languages. In this paper, we introduce OffMix-3L, a novel offensive language identification dataset containing code-mixed data from three different languages. We experiment with several models on this dataset and observe that BanglishBERT outperforms other transformer-based models and GPT-3.5.

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