一种基于机器学习的外语翻译系统设计

A Foreign Language Translation System Design Based On Machine Learning

外语翻译高效性一直是行业专家研究的重点,如何利用机器学习算法提升外语翻译质量是业界重点研究方向。针对一些资源贫乏、形态丰富的小语种,因缺乏大量的训练数据,传统的机器学习模型无法获取较好的翻译效果,本文使用合成数据来提高仅使用真实数据构建的模型所产生的翻译质量,通过两方面途径实现翻译质量的提升。

The efficiency of foreign language translation is the focus of research by industry experts. How to use machine learning algorithms to improve the quality of foreign language translation is a key research direction in the industry. In view of some small languages with poor resources and rich forms, traditional machine learning models cannot obtain good translation results due to the lack of a large amount of training data. This article uses synthetic data to improve the translation quality generated by models constructed using only real data. The improvement of translation quality is achieved through the two ways.