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.
OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification
A new offensive language identification dataset, OffMix-3L, featuring three-language code-mixed data is introduced, with BanglishBERT outperforming other transformer-based models and GPT-3.5.
- Year
- 2023
- Venue
- arXiv 2023
- Authors
- 5
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2310.18387v2ARXIV-DEFAULT
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