Hate Speech Detection in Limited Data Contexts using Synthetic Data
  Generation
Aman Khullar Daniel Nkemelu Cuong V. Nguyen Michael L. Best
A growing body of work has focused on text classification methods for
detecting the increasing amount of hate speech posted online. This progress has
been limited to only a select number of highly-resourced languages causing
detection systems to either under-perform or not exist in limited data
contexts. This is majorly caused by a lack of training data which is expensive
to collect and curate in these settings. In this work, we propose a data
augmentation approach that addresses the problem of lack of data for online
hate speech detection in limited data contexts using synthetic data generation
techniques. Given a handful of hate speech examples in a high-resource language
such as English, we present three methods to synthesize new examples of hate
speech data in a target language that retains the hate sentiment in the
original examples but transfers the hate targets. We apply our approach to
generate training data for hate speech classification tasks in Hindi and
Vietnamese. Our findings show that a model trained on synthetic data performs
comparably to, and in some cases outperforms, a model trained only on the
samples available in the target domain. This method can be adopted to bootstrap
hate speech detection models from scratch in limited data contexts. As the
growth of social media within these contexts continues to outstrip response
efforts, this work furthers our capacities for detection, understanding, and
response to hate speech.