Cross-Domain Toxic Spans Detection
Stefan F. Schouten Baran Barbarestani Wondimagegnhue Tufa Piek Vossen Ilia Markov
Given the dynamic nature of toxic language use, automated methods for
detecting toxic spans are likely to encounter distributional shift. To explore
this phenomenon, we evaluate three approaches for detecting toxic spans under
cross-domain conditions: lexicon-based, rationale extraction, and fine-tuned
language models. Our findings indicate that a simple method using off-the-shelf
lexicons performs best in the cross-domain setup. The cross-domain error
analysis suggests that (1) rationale extraction methods are prone to false
negatives, while (2) language models, despite performing best for the in-domain
case, recall fewer explicitly toxic words than lexicons and are prone to
certain types of false positives. Our code is publicly available at:
https://github.com/sfschouten/toxic-cross-domain.