Abstract
The presence of offensive language on social media is very common motivating platforms to invest in strategies to make communities safer. This includes developing robust machine learning systems capable of recognizing offensive content online. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English and a few other high-resource languages such as French, German, and Spanish. In this paper, we address this gap by tackling offensive language identification in Marathi, a low-resource Indo-Aryan language spoken in India. We introduce the Marathi Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded annotation to the levels B (type) and C (target) of the popular OLID taxonomy. MOLD 2.0 is the first hierarchical offensive language dataset compiled for Marathi, thus opening new avenues for research in low-resource Indo-Aryan languages. Finally, we also introduce SeMOLD, a larger dataset annotated following the semi-supervised methods presented in SOLID (Rosenthal et al. in SOLID: a large-scale semi-supervised dataset for offensive language identification. In: Findings of ACL, 2021).
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Notes
Dataset available at: https://github.com/tharindudr/MOLD.
Tweepy Python library documentation is available on https://www.tweepy.org/.
Marathi FastText embeddings are available on https://fasttext.cc/docs/en/crawl-vectors.html.
Marathi word embeddings are available on https://www.cfilt.iitb.ac.in/~diptesh/embeddings/.
DeepOffense is available as a pip package in https://pypi.org/project/deepoffense/.
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Zampieri, M., Ranasinghe, T., Chaudhari, M. et al. Predicting the type and target of offensive social media posts in Marathi. Soc. Netw. Anal. Min. 12, 77 (2022). https://doi.org/10.1007/s13278-022-00906-8
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DOI: https://doi.org/10.1007/s13278-022-00906-8