Code-switching entails mixing multiple languages. It is an increasingly
occurring phenomenon in social media texts. Usually, code-mixed texts are
written in a single script, even though the languages involved have different
scripts. Pre-trained multilingual models primarily utilize the data in the
native script of the language. In existing studies, the code-switched texts are
utilized as they are. However, using the native script for each language can
generate better representations of the text owing to the pre-trained knowledge.
Therefore, a cross-language-script knowledge sharing architecture utilizing the
cross attention and alignment of the representations of text in individual
language scripts was proposed in this study. Experimental results on two
different datasets containing Nepali-English and Hindi-English code-switched
texts, demonstrate the effectiveness of the proposed method. The interpretation
of the model using model explainability technique illustrates the sharing of
language-specific knowledge between language-specific representations.

本研究提出了一种跨语言脚本知识共享架构，利用交叉关注和语言脚本的对齐来生成更好的文本表示，实验证明了该方法的有效性，并通过模型可解释性技术解释了语言特定表示之间的知识共享。