Offensive language detection in Hebrew: can other languages help?

Marina Litvak, Natalia Vanetik, Chaya Liebeskind, Omar Hmdia, Rizek Abu Madeghem

Abstract
Unfortunately, offensive language in social media is a common phenomenon nowadays. It harms many people and vulnerable groups. Therefore, automated detection of offensive language is in high demand and it is a serious challenge in multilingual domains. Various machine learning approaches combined with natural language techniques have been applied for this task lately. This paper contributes to this area from several aspects: (1) it introduces a new dataset of annotated Facebook comments in Hebrew; (2) it describes a case study with multiple supervised models and text representations for a task of offensive language detection in three languages, including two Semitic (Hebrew and Arabic) languages; (3) it reports evaluation results of cross-lingual and multilingual learning for detection of offensive content in Semitic languages; and (4) it discusses the limitations of these settings.
Anthology ID:
2022.lrec-1.396
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3715–3723
Language:
URL:
https://aclanthology.org/2022.lrec-1.396
DOI:
Bibkey:
Cite (ACL):
Marina Litvak, Natalia Vanetik, Chaya Liebeskind, Omar Hmdia, and Rizek Abu Madeghem. 2022. Offensive language detection in Hebrew: can other languages help?. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3715–3723, Marseille, France. European Language Resources Association.
Cite (Informal):
Offensive language detection in Hebrew: can other languages help? (Litvak et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-2/2022.lrec-1.396.pdf
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