The Impact of Pre-Training and Fine-Tuning on Machine Translation Accuracy

Abstract

This paper investigates the impact of pre-training and fine-tuning on the accuracy of machine translation systems. Pre-training involves training a language model on a large corpus of text data to learn language representations, which are then used as a foundation for various downstream tasks, including translation. Fine-tuning further refines the model on specific translation datasets to improve performance in targeted language pairs and domains. The results demonstrate that pre-training significantly enhances the initial translation capabilities of models by providing a strong linguistic foundation. Fine-tuning on specific translation datasets yields substantial improvements in translation accuracy, particularly in terms of fluency and adequacy. This study underscores the importance of large-scale pre-training for creating versatile language models and highlights the critical role of fine-tuning in adapting these models to specific translation tasks. Future research directions include exploring more efficient fine-tuning techniques and extending these methods to low-resource languages to ensure broader applicability and inclusivity in machine translation technologies.