Machine translation (MT) is an artificial intelligence technology that uses computers to automatically convert one natural language into another. This technology combines linguistics, statistical modeling, and deep learning methods, enabling the system to analyze the semantic information of source language text and generate corresponding expressions in the target language. It is one of the important research directions in the field of natural language processing.

Machine translation has a long history, with research dating back to the mid-20th century. Early methods primarily relied on rule-based machine translation systems, using manually written language rules and dictionaries to complete translations. Subsequently, statistical machine translation (SMT), utilizing large-scale bilingual corpora to learn the correspondences between words and phrases, became the mainstream method for machine translation around 2000. With the development of deep learning, neural machine translation (NMT) has gradually emerged as a new technological approach. In 2015, researchers from the University of Montreal and Jacobs University published a paper... Neural Machine Translation by Jointly Learning to Align and Translate The paper proposes a neural machine translation model based on encoder-decoder and attention mechanisms. By jointly learning word alignment relationships and the translation process, it has become one of the important representative studies in the field of neural machine translation.

Compared to traditional statistical methods, neural machine translation (NNMT) can better utilize contextual information, improving translation quality in long sentences and complex contexts. In recent years, translation systems have gradually shifted from "training one model for one language pair" to large-scale multilingual models like NLLB, which can cover more than two hundred languages (including many low-resource languages). General-purpose large language models are also beginning to directly undertake cross-language translation tasks, no longer relying on specialized training for specific language pairs. Currently, this technology is widely used in cross-language search, international communication, cross-border e-commerce, content localization, assisted translation, and multilingual information processing.