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

Dhiman Goswami, Md Nishat Raihan, Antara Mahmud, Antonios Anastasopoulos, Marcos Zampieri
10/27/2023
cs.CLcs.AI

No official code found. Best implementation: LanguageTechnologyLab/OffMix-3L (3 stars, AGPL-3.0, last commit 2023-11-08).

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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Code Implementations(1)

LanguageTechnologyLab/OffMix-3LCommunity100%
AGPL-3.0

This is a dataset for the offensive language detection task. It contains 1k natural code mixed data. The languages are Bangla-English-Hindi.

31Oct 17, 20232 years agoAGPL-3.0

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Cite this paper

@article{goswami2023offmixl,
  title  = {OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification},
  author = {Dhiman Goswami and Md Nishat Raihan and Antara Mahmud and Antonios Anastasopoulos and Marcos Zampieri},
  year   = {2023},
  eprint = {2310.18387},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.18387v2}
}

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