XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages

Goran Glavaš, Mladen Karan, Ivan Vulić


Abstract
We present XHate-999, a multi-domain and multilingual evaluation data set for abusive language detection. By aligning test instances across six typologically diverse languages, XHate-999 for the first time allows for disentanglement of the domain transfer and language transfer effects in abusive language detection. We conduct a series of domain- and language-transfer experiments with state-of-the-art monolingual and multilingual transformer models, setting strong baseline results and profiling XHate-999 as a comprehensive evaluation resource for abusive language detection. Finally, we show that domain- and language-adaption, via intermediate masked language modeling on abusive corpora in the target language, can lead to substantially improved abusive language detection in the target language in the zero-shot transfer setups.
Anthology ID:
2020.coling-main.559
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6350–6365
Language:
URL:
https://aclanthology.org/2020.coling-main.559
DOI:
10.18653/v1/2020.coling-main.559
Bibkey:
Cite (ACL):
Goran Glavaš, Mladen Karan, and Ivan Vulić. 2020. XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6350–6365, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages (Glavaš et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.559.pdf
Data
Xhate999