Zero-shot cross-lingual transfer learning has been shown to be highly
challenging for tasks involving a lot of linguistic specificities or when a
cultural gap is present between languages, such as in hate speech detection. In
this paper, we highlight this limitation for hate speech detection in several
domains and languages using strict experimental settings. Then, we propose to
train on multilingual auxiliary tasks -- sentiment analysis, named entity
recognition, and tasks relying on syntactic information -- to improve zero-shot
transfer of hate speech detection models across languages. We show how hate
speech detection models benefit from a cross-lingual knowledge proxy brought by
auxiliary tasks fine-tuning and highlight these tasks' positive impact on
bridging the hate speech linguistic and cultural gap between languages.

本文研究了零 - shot 语言跨域转移学习在对抗言检测等任务中所面临的挑战，并提出使用多语种辅助任务（情感分析、命名实体识别和依靠语法信息的任务）进行训练，以提高模型的零 - shot 转移到不同语言的能力，最终实现跨语言之间的言语和文化差异的缩小。