The continuous development of artificial intelligence (AI) has also brought great changes to the translation industry. Traditional manual translation has gradually been replaced, and AI translation has gradually been accepted and used by businesses for its advantages of fast translation speed, high efficiency and low cost.
7.1 AI’s three main methods of translating human natural language
AI mainly uses the following three methods for translation: rule-based machine translation method, example-based machine translation method, and statistical-based translation method.
7.1.1 Rule-based machine translation method
The data repository of AI’s rule-based machine translation method is composed of dictionary content and rule library, which is a language result obtained through calculation. The machine translation system was born and developed with corpus science. Most machine translation systems adopt a strategy of translation based on rules. The purpose of translation into the target language is achieved by disassembling and analyzing the original language and converting the data repository of both languages through AI machines.
7.1.2 Case-Based Machine Translation Method
Case-Based machine translation method, its translation performance depends on the quality of the case library. The machine stores translation examples in a fragmented manner and stores more grammatical and semantic information in the case library to improve the accuracy of case translation. The original language is preprocessed by word segmentation, part-of-speech tagging and syntactic analysis at the same time, which also leads to the iterative transmission of existing errors between tasks and affects the accuracy and reliability of structured examples.
7.1.3 Statistics-Based Translation Method
The statistics-based translation method is to summarize all possible translation results of words and phrases in the original language, and then search them in a huge corpus, count the probability of each result, and output the translation result with the highest probability to achieve the translation purpose. This method is better than the regular translation method, and at the same time it is more dependent on the corpus.