从规则到生成：机器翻译技术的演进，现状及未来发展趋势

Abstract： With the rapid development of artificial inteligence and deep learning technologies， machine translation is playing an increasingly important role in facilitating cross-language communication.This paper systematically reviews the four evolutionary stages of machine translation technology， from the early rule-based systems， the statistical methods based on large-scale data， and the neural machine translation based on deep learning，to the current generative artificial intelligence （GenAl） translation models. It also shows that，although GenAl translation models have made significant progress in translation quality and efficiency，they still face problems such as data scarcity，limited model generalization ability， incomplete evaluation mechanisms，and lack of interpretability and ethical cultural sensitivity. This paper suggests that the future development of machine translation technology focus on enhancing the generalization abilityand interpretability of models， developing more comprehensive evaluation tools， and ensuring the cultural adaptability and ethical compliance of translation systems-all in seek of greater potential of machine translation ina wider range of application scenarios.

Key words： translation technology； machine translation ； generative artificial inteligence （GenAl）

1.引言

全球化进程加速和数字化转型深人使得不同语言和文化间的交流越来越频繁，对高质量机器翻译技术的需求也愈发迫切（范梦栩、皮姆，2021）。（剩余13483字）