Share What You Already Know: Cross-Language-Script Transfer and
  Alignment for Sentiment Detection in Code-Mixed Data
Niraj Pahari Kazutaka Shimada
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.