Unlocking Linguistic Borders: Leveraging Cross-Lingual Transfer Learning for Enhanced Machine Translation

Abstract

This paper explores the application of cross-lingual transfer learning techniques to enhance machine translation performance. By leveraging the knowledge encoded in pre-trained models across multiple languages, we investigate how transfer learning can facilitate more accurate and fluent translations. Our study focuses on the effectiveness of transferring knowledge from high-resource languages to low-resource languages, addressing the challenge of data scarcity in the latter. We demonstrate the benefits of cross-lingual transfer learning in improving translation quality, especially for languages with limited available resources. Through experiments and evaluations on various language pairs, we analyze the impact of different transfer learning approaches and model architectures on translation accuracy and efficiency. The findings underscore the potential of cross-lingual transfer learning as a promising strategy for breaking down linguistic barriers and advancing machine translation capabilities on a global scale.