A Checkpoint on Multilingual Misogyny Identification

Abstract

We address the problem of identifying misogyny in tweets in mono and multilingual settings in three languages: English, Italian and Spanish. We explore model variations considering single and multiple languages both in the pre-training of the transformer and in the training of the downstream task to explore the feasibility of detecting misogyny through a transfer learning approach across multiple languages. That is, we train monolingual transformers with monolingual data and multilingual transformers with both monolingual and multilingual data. Our models reach state-of-the-art performance on all three languages. The single-language BERT models perform the best, closely followed by different configurations of multilingual BERT models. The performance drops in zero-shot classification across languages. Our error analysis shows that multilingual and monolingual models tend to make the same mistakes.