Towards multidomain and multilingual abusive language detection: a survey

By Endang Wahyu Pamungkas, Valerio Basile, Viviana Patti. 2021

46 citations recorded by the source corpus.

Abusive language is an important issue in online communication across different platforms and languages. Having a robust model to detect abusive instances automatically is a prominent challenge. Several studies have been proposed to deal with this vital issue by modeling this task in the cross-domain and cross-lingual setting. This paper outlines and describes the current state of this research direction, providing an overview of previous studies, including the available datasets and approaches employed in both cross-domain and cross-lingual settings. This study also outlines several challenges and open problems of this area, providing insights and a useful roadmap for future work.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “However, research on associated risks and their mitigation in these multilingual models has not even remotely caught up (Pamungkas et al., 2021).”