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Offensive language detection in Hebrew: can other languages help?

What it is about

The authors build a binary offensive-language classifier for Hebrew, using a new set of 5217 annotated Facebook comments (two labels per comment, Kappa 0.82), and compare it with Arabic (9000 tweets) and English (OLID, 13 240 tweets). Three classifiers (random forest, logistic regression, RBF-SVM) are run on three representations: tf*idf bag of words, character 1–3-grams, and multilingual BERT sentence vectors. In monolingual tests mBERT gives the best F-measure in 5 of 9 cases, and BOW gives the best precision in 7 of 9. Cross-lingual transfer from Arabic to Hebrew raises Hebrew recall, but the other transfers are worse than monolingual; multilingual training with Arabic is close to monolingual results. The authors name tokenisation of prefixed Hebrew forms as a known difficulty for BOW.

Ideas

For Mova

I1 is a measurement for tools/hbo and the tokenisation chapter 2. Script and spelling. I2 is a rule for any cross-lingual test in 8. Lexicon and register (offensive register). I3 is a preprocessing step that belongs to tools/langseg. The classifiers themselves are not Mova's approach; only the BOW baseline is transparent enough to keep as a comparison.

Cite

The paper itself, not this card. BibTeX from the library's record of the work.

@inproceedings{litvak2022offensive,
  title = {{Offensive language detection in Hebrew: can other languages help?}},
  author = {Litvak, Marina and Vanetik, Natalia and Liebeskind, Chaya and Hmdia, Omar and Madeghem, Rizek Abu},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  year = {2022},
  url = {https://aclanthology.org/2022.lrec-1.396/}
}

References

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