Recent advances in cross-lingual commonsense reasoning (CSR) are facilitated by the development of multilingual pre-trained models (mPTMs). While mPTMs show the potential to encode commonsense knowledge for different languages, transferring commonsense knowledge learned in large-scale English corpus to other languages is challenging. To address this problem, we propose the attention-based Cross-LIngual Commonsense Knowledge transfER (CLICKER) framework, which minimizes the performance gaps between English and non-English languages in commonsense question-answering tasks. CLICKER effectively improves commonsense reasoning for non-English languages by differentiating non-commonsense knowledge from commonsense knowledge. Experimental results on public benchmarks demonstrate that CLICKER achieves remarkable improvements in the cross-lingual CSR task for languages other than English.

翻译：近期跨语言常识推理（CSR）的进展得益于多语言预训练模型（mPTMs）的发展。尽管mPTMs展现了为不同语言编码常识知识的潜力，但将在大规模英语语料中习得的常识知识迁移至其他语言仍具挑战性。针对此问题，我们提出基于注意力的跨语言常识知识迁移（CLICKER）框架，该框架通过区分常识知识与非常识知识，最小化英语与非英语语言在常识问答任务中的性能差距。在公开基准上的实验结果表明，CLICKER在非英语语言的跨语言CSR任务中实现了显著提升。