Addressing the Challenges of Cross-Lingual Hate Speech Detection

Irina Bigoulaeva, Viktor Hangya, Iryna Gurevych, Alexander Fraser

https://arxiv.org/abs/2201.05922


The goal of hate speech detection is to filter negative online content
aiming at certain groups of people. Due to the easy accessibility of
social media platforms it is crucial to protect everyone which
requires building hate speech detection systems for a wide range of
languages. However, the available labeled hate speech datasets are
limited making it problematic to build systems for many languages. In
this paper we focus on cross-lingual transfer learning to support hate
speech detection in low-resource languages. We leverage cross-lingual
word embeddings to train our neural network systems on the source
language and apply it to the target language, which lacks labeled
examples, and show that good performance can be achieved. We then
incorporate unlabeled target language data for further model
improvements by bootstrapping labels using an ensemble of different
model architectures. Furthermore, we investigate the issue of label
imbalance of hate speech datasets, since the high ratio of non-hate
examples compared to hate examples often leads to low model
performance. We test simple data undersampling and oversampling
techniques and show their effectiveness.