Unreferenced English articles’ translation quality-oriented automatic evaluation technology using sparse autoencoder under the background of deep learning

Abstract

Currently, both manual and automatic evaluation technology can evaluate the translation quality of unreferenced English articles, playing a particular role in detecting translation results. Still, their deficiency is the lack of a close or noticeable relationship between evaluation time and evaluation theory. Thereupon, to realize the automatic Translation Quality Assessment (TQA) of unreferenced English articles, this paper proposes an automatic TQA model based on Sparse AutoEncoder (SAE) under the background of Deep Learning (DL). Meanwhile, the DL-based information extraction method employs AutoEncoder (AE) in the bilingual words’ unsupervised learning stage to reconstruct the translation language vector features. Then, it imports the translation information of unreferenced English articles into Bilingual words and optimizes the extraction effect of language vector features. Meantime, the translation language vector feature is introduced into the automatic DL-based TQA. The experimental findings corroborate that when the number of sentences increases, the number of actual translation errors and the evaluation scores of the proposed model increase, but the Bilingual Evaluation Understudy (BLEU) score is not significantly affected. When the number of sentences increases from 1,000 to 6,000, the BLEU increases from 96 to 98, which shows that the proposed model has good performance. Finally, the proposed model can realize the high-precision TQA of unreferenced English articles.