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- W4380519650 abstract "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 ." @default.
- W4380519650 created "2023-06-14" @default.
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- W4380519650 date "2023-01-01" @default.
- W4380519650 modified "2023-09-27" @default.
- W4380519650 title "Cross-Domain Toxic Spans Detection" @default.
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- W4380519650 doi "https://doi.org/10.1007/978-3-031-35320-8_40" @default.
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