Deep Learning Models for Multilingual Hate Speech Detection
Table 3: Monolingual scenario: the training, validation and testing data is used from the same language. Here, Full D represents the full training data. The bold figures represent the best scores and underline represents the second best.
['Language', 'Model', 'Training Size 16', 'Training Size 32', 'Training Size 64', 'Training Size 128', 'Training Size 256', 'Training Size Full D']
Observations: As expected, we observe that with increasing training data, the classifier performance increases as well. However, the relative performance seem to vary depending on the language and the model. We make several observations. First, LASER + LR performs the best in low-resource settings (16,32,64,128,256) for all the languages. Second, we observe that MUSE + CNN-GRU performs the worst in almost all the cases. Third, Translation + BERT seems to achieve competitive performance for some of the languages such as German, Polish, Portuguese, and Spanish. Overall we observe that there is no ‘one single recipe’ for all languages; however, Translation + BERT seems to be an excellent compromise. We believe that improved translations in some languages can further improve the performance of this model.