  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.
