Cross-Lingual Transfer Learning for Hate Speech Detection

Irina Bigoulaeva, Viktor Hangya, Alexander Fraser

Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion (LT-EDI)

April 2021

We address the task of automatic hate speech detection for
low-resource languages. Rather than collecting and annotating new hate
speech data, we show how to use cross-lingual transfer learning to
leverage already existing data from higher-resource languages. Using
bilingual word embeddings based classifiers we achieve good
performance on the target language by training only on the source
dataset. Using our transferred system we bootstrap on unlabeled target
language data, improving the performance of standard cross-lingual
transfer approaches. We use English as a high resource language and
German as the target language for which only a small amount of
annotated corpora are available. Our results indicate that
cross-lingual transfer learning together with our approach to leverage
additional unlabeled data is an effective way of achieving good
performance on low-resource target languages without the need for any
target-language annotations.