  The growing interest in employing counter narratives for hatred intervention
brings with it a focus on dataset creation and automation strategies. In this
scenario, learning to recognize counter narrative types from natural text is
expected to be useful for applications such as hate speech countering, where
operators from non-governmental organizations are supposed to answer to hate
with several and diverse arguments that can be mined from online sources. This
paper presents the first multilingual work on counter narrative type
classification, evaluating SoTA pre-trained language models in monolingual,
multilingual and cross-lingual settings. When considering a fine-grained
annotation of counter narrative classes, we report strong baseline
classification results for the majority of the counter narrative types,
especially if we translate every language to English before cross-lingual
prediction. This suggests that knowledge about counter narratives can be
successfully transferred across languages.
