Cross-lingual zero-and few-shot hate speech detection utilising frozen transformer language models and AXEL
- Detecting hate speech, especially in low-resource languages, is a non-trivial challenge. To tackle this, we developed a tailored architecture based on frozen, pre-trained Transformers to examine cross-lingual zero-shot and few-shot learning, in addition to uni-lingual learning, on the HatEval challenge data set. With our novel attention-based classification block AXEL, we demonstrate highly competitive results on the English and Spanish subsets. We also re-sample the English subset, enabling additional, meaningful comparisons in the future.
| Author: | Lukas Stappen, Fabian Brunn, Björn W. SchullerORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-917123 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/91712 |
| Parent Title (English): | arXiv |
| Type: | Preprint |
| Language: | English |
| Date of Publication (online): | 2022/01/05 |
| Year of first Publication: | 2020 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2022/01/28 |
| First Page: | arXiv:2004.13850v1 |
| DOI: | https://doi.org/10.48550/arXiv.2004.13850 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing | |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Licence (German): |  Deutsches Urheberrecht |
