Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi

Saurabh Gaikwad Gaikwad, Tharindu Ranasinghe, Marcos Zampieri, Christopher Homan

    Research output: Chapter in Book/Published conference outputConference publication


    The widespread presence of offensive language on social media motivated the development of systems capable of recognizing such content automatically. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English. To address this shortcoming, we introduce MOLD, the Marathi Offensive Language Dataset. MOLD is the first dataset of its kind compiled for Marathi, thus opening a new domain for research in low-resource Indo-Aryan languages. We present results from several machine learning experiments on this dataset, including zero-short and other transfer learning experiments on state-of-the-art cross-lingual transformers from existing data in Bengali, English, and Hindi.
    Original languageEnglish
    Title of host publicationProceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
    Number of pages7
    Publication statusPublished - Sept 2021

    Bibliographical note

    This accepted manuscript is distributed under the terms of the Creative Commons Attribution License CC BY [], which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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