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Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL

2020-04-13 09:58:33
Lukas Stappen, Fabian Brunn, Björn Schuller
arXiv_CL
                 
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2004.13850

PDF

https://arxiv.org/pdf/2004.13850.pdf

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