Resource-Rich Machine Translation: The Role of Pre-Training vs. Random Initialization

Abstract

In recent years, machine translation (MT) has undergone significant advancements driven by the increasing availability of large-scale parallel corpora and the emergence of deep learning techniques. This paper explores the critical role of pre-training in resource-rich machine translation settings, contrasting it with models initialized randomly. By analyzing various approaches to pre-training and the impact on translation performance, we demonstrate that leveraging pre-trained models significantly enhances translation quality across diverse languages and domains. We also discuss the implications of these findings for the development of future translation systems, emphasizing the need for strategic pre-training methods that maximize the use of available resources. Our findings underscore that while random initialization has its merits, pre-training is pivotal in achieving state-of-the-art results in machine translation tasks.