# CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer

## TL;DR

CLICKER is an attention-based framework that enhances cross-lingual commonsense reasoning by transferring knowledge from English to other languages, significantly improving performance on multilingual benchmarks.

## Contribution

It introduces an attention-based transfer method that effectively minimizes language gaps in cross-lingual commonsense reasoning tasks.

## Key findings

- Achieves significant improvements on multilingual benchmarks.
- Effectively differentiates between commonsense and non-commonsense knowledge.
- Reduces performance gaps between English and non-English languages.

## Abstract

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.