Diversified Research and Analysis of Machine Translation Models and the Development Prospects of Translation for Less-Commonly Spoken Language

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This paper begins by addressing issues in machine translation, reviewing the current state of research on translation problems, and summarizing various mainstream machine translation models. It also analyzes the structural characteristics and advantages of each model. Based on the current research landscape, mainstream machine translation models generally underperform in scenarios involving less-commonly spoken languages. Therefore, future research needs to focus on breakthroughs in such scenarios. Preliminary findings from recent studies indicate that advanced techniques such as data augmentation, reinforcement learning, and transfer learning have significantly contributed to addressing these issues. Future research could integrate these theoretical approaches to further enhance the quality of machine translation.