Multilingual pretrained language models serve as repositories of multilingual
factual knowledge. Nevertheless, a substantial performance gap of factual
knowledge probing exists between high-resource languages and low-resource
languages, suggesting limited implicit factual knowledge transfer across
languages in multilingual pretrained language models. This paper investigates
the feasibility of explicitly transferring relatively rich factual knowledge
from English to non-English languages. To accomplish this, we propose two
parameter-free $\textbf{L}$anguage $\textbf{R}$epresentation
$\textbf{P}$rojection modules (LRP2). The first module converts non-English
representations into English-like equivalents, while the second module reverts
English-like representations back into representations of the corresponding
non-English language. Experimental results on the mLAMA dataset demonstrate
that LRP2 significantly improves factual knowledge retrieval accuracy and
facilitates knowledge transferability across diverse non-English languages. We
further investigate the working mechanism of LRP2 from the perspectives of
representation space and cross-lingual knowledge neuron.

通过两个无参数的语言表示投影模块（LRP2），本文调查了从英语到非英语语言的显式传输相对丰富的事实知识的可行性，并在 mLAMA 数据集上的实验结果证明了 LRP2 显著提高了事实知识检索精度，促进了对多种非英语语言的知识传递能力。