  Multilingual pre-trained language models can learn task-specific abilities or
memorize facts across multiple languages but inevitably make undesired
predictions with specific inputs. Under similar observation, model editing aims
to post-hoc calibrate a model targeted to specific inputs with keeping the
model's raw behavior. However, existing work only studies the monolingual
scenario, which lacks the cross-lingual transferability to perform editing
simultaneously across languages. In this work, we focus on cross-lingual model
editing. Firstly, we define the cross-lingual model editing task and
corresponding metrics, where an edit in one language propagates to the others.
Next, we propose a framework to naturally adapt monolingual model editing
approaches to the cross-lingual scenario using parallel corpus. Further, we
propose language anisotropic editing to improve cross-lingual editing by
amplifying different subsets of parameters for each language. On the newly
defined cross-lingual model editing task, we empirically demonstrate the
failure of monolingual baselines in propagating the edit to multiple languages
and the effectiveness of the proposed language anisotropic model editing. Our
code is publicly available at https://github.com/franklear/LiME.
