Pre-trained language models learn large amounts of knowledge from their
training corpus, while the memorized facts could become outdated over a few
years. Model editing aims to make post-hoc updates on specific facts in a model
while leaving irrelevant knowledge unchanged. However, existing work studies
only the monolingual scenario. In this paper, we focus on cross-lingual model
editing. Firstly, we propose the definition and metrics of the cross-lingual
model editing, where updates in a single language should take effect in the
others as well. Next, we propose a simple framework to convert a monolingual
model editing approach to its cross-lingual variant using the parallel corpus.
Experiments show that such an approach outperforms monolingual baselines by a
large margin. Furthermore, we propose language anisotropic editing to improve
cross-lingual editing by estimating parameter importance for each language.
Experiments reveal that language anisotropic editing decreases the editing
failing rate by another $26\%$ relatively.

本文提出了适用于跨语言模型的模型编辑方法，使用平行语料库实现了单语言模型编辑方法的跨语言变体，并采用了语言各向异性编辑方法，实现了显着的编辑率下降。