A Metric Learning Approach to Misogyny Categorization

WS 2020 Juan Manuel CoriaSahar GhannaySophie RossetHerv{\'e} Bredin

The task of automatic misogyny identification and categorization has not received as much attention as other natural language tasks have, even though it is crucial for identifying hate speech in social Internet interactions. In this work, we address this sentence classification task from a representation learning perspective, using both a bidirectional LSTM and BERT optimized with the following metric learning loss functions: contrastive loss, triplet loss, center loss, congenerous cosine loss and additive angular margin loss... (read more)

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