Improving Cross-Lingual Model Performance Using Adaptive Representations and Bilingual Lexicon Induction Techniques

Abstract

Cross-lingual models have shown significant promise in bridging language barriers in various applications. This paper presents novel approaches to enhance the performance of cross-lingual models through adaptive representations and bilingual lexicon induction techniques. We explore methods to create more robust and accurate language representations and techniques for automatically generating bilingual lexicons. Our experimental results demonstrate the effectiveness of these approaches in improving model performance across multiple languages.