IELTS translation education corpus construction based on bilingual non-parallel data model

Abstract

With the rapid development of intelligent technology, IELTS translation education has gradually explored a brand-new education model. In order to solve the problems of small scale, slow speed and incomplete domain of the traditional bilingual parallel corpus machine translation, we construct an IELTS translation education corpus based on bilingual non-parallel data model, which can be used to train Moses, an IELTS translation education machine translation model, for better aids of translation education. In the process of construction, parallel sentence pairs are extracted from non-parallel corpus by using the translation retrieval framework represented by word graph, and a translation retrieval model based on bilingual non-parallel data is constructed. The experimental results of training Moses translation model with elementary IELTS translation corpus show that the bilingual non-parallel data model constructed in this paper has good translation retrieval performance. Compared with existing algorithms, the BLEU value extracted from parallel sentence pairs is increased by 2.58. Therefore, the proposed algorithm and corpus will do favor for the machine translation of IELTS. In addition, the retrieval method based on the structure of translation option word graph proposed in this paper is time-efficient and has better performance and efficiency in assisting IELTS translation education.