Enhancing Cross-Lingual Aspect-Based Sentiment Analysis with Code-Mixed In-Context Demonstrations and Language-Specific Tags

Cross-lingual Aspect-based Sentiment Analysis (XABSA) aims to extract aspect-level sentiments across multiple languages. This task typically relies on source language data to train models and transfer them to target languages, so it faces significant challenges such as data scarcity and language disparities. To this end, this study proposes a code-Mixed In-conteXt lEaRning (MIXER). We design four kinds of demonstration retrieval libraries to introduce Code-mixed In-Context Demonstrations (CICD), which use the code-mixed mechanism to integrate the features of the target language and enrich the target language's knowledge while retaining the source language's knowledge. Language-Specific tags (LST) are introduced to enhance the model's understanding of multilingual demonstrations. To validate the effectiveness of MIXER, we conduct extensive experiments on the SemEval-2016 dataset, comparing its performance against existing XABSA methods. The experimental results show that MIXER performs better than existing XABSA methods with average F1 scores on the Mistral and Llama3 improved by 1.59% and 1.44%, respectively, highlighting its potential for broader multilingual applications.