Cross-lingual transfer Learning for Enhanced Machine Translation

Abstract

Cross-lingual transfer learning has emerged as a promising approach for enhancing machine translation (MT) systems, especially in low-resource language pairs. By leveraging knowledge from high-resource languages, transfer learning enables MT models to generalize better and produce more accurate translations. This paper provides an overview of cross-lingual transfer learning techniques in MT, discusses their advantages and challenges, and explores their applications in various scenarios. The paper discusses various cross-lingual transfer learning strategies, including pre-training multilingual models, transfer learning via pivot languages, and zero-shot translation. These techniques enable MT systems to generalize across languages, adapt to diverse linguistic contexts, and improve translation accuracy, even for languages with limited training data. Furthermore, the paper examines the benefits of cross-lingual transfer learning in specific scenarios, such as low-resource languages, specialized domains, and user-generated content. By leveraging shared linguistic features and transferable knowledge across languages, cross-lingual transfer learning enhances the quality and availability of translations, enabling effective communication across language barriers.