A Review of Mongolian Neural Machine Translation from the Perspective of Training
Yatu Ji, Zhang Huinuan, Nier Wu, Ren Ji, Min Lu, Shi Bao · 2024
The characteristics of neural machine translation require training and updating through a large number of corpus for hundreds of millions of parameters. In this situation, Mongolian Neural Machine Translation(MNMT) needs a variety of targeted training techniques and additional strategies to alleviate the problems caused by resource scarcity. These problems run through the whole translation process. taking the key steps of model training as a clue, This paper conducted detailed experiments and analysis on the main training content, and summarizes the relevant research and key issues according to the mainstream training processes such as ‘corpus processing→word embedding training→parameter pre-training→end-to-end model training→translation key problem analysis’. On this basis, this paper is committed to some long-standing stubborn problems to give a review of the treatment methods and training suggestions, and to provide some references for other researchers.