Research on Statistical Machine Translation of Bilingual Resources Lacking Language Pairs Based on Active Learning

Abstract

The research on machine translation for bilingual resources and lack of language pairs is a hot and difficult issue in the field of machine translation. Based on the research background of statistical machine translation with language pairs lacking in resources, this paper uses active learning strategy to design an effective sentence selection algorithm rich in information, obtain high-quality bilingual data from large-scale monolingual corpus, and give full play to the maximum efficiency of limited bilingual data, so as to significantly improve the performance of statistical machine translation for language pairs lacking in bilingual resources. This is a research project aimed at developing statistical machine translation of bilingual resources lacking language pairs. The main idea of this study is to use active learning technology to improve the quality of translation, so as to increase the number of available translations of each resource. In other words, by using active learning methods, it will be possible to translate more languages into more accurate fields. The main benefit of this research project is that it will provide many new resources and languages that were previously inaccessible due to the lack of language pairs.