  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.
