A novel domain adaption approach for neural machine translation. (26th August 2020)

• Record Type: Journal Article
• Authors: Liu, Jin; Zhang, Xin; Tian, Xiaohu; Wang, Jin; Sangaiah, Arun Kumar
• Is Part Of: International journal of computational science and engineering. Volume 22:Number 4(2020)
• Page Start: 445
• Page End: 453
• Publication Date: 2020-08-26
• Subjects: neural machine translation -- model fusion -- domain adaption; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Neural machine translation has been widely adopted in modern machine translation as it brings the state-of-the-art performance to large-scale parallel corpora. For real-world applications, high-quality translation for text in a specific domain is crucial. However, performances of general neural machine models drop when being applied in a specific domain. To alleviate this issue, this paper presents a novel method of machine translation, which explores both model fusion algorithm and logarithmic linear interpolation. The method can improve the performance of in-domain translation model, while preserving or even improving the performance of out-domain translation model. This paper has carried out extensive experiments on proposed translation model using the public United Nations corpus. The bilingual evaluation understudy (BLEU) score of the in-domain corpus and the out-domain corpus reaches 30.27 and 43.17 respectively, which shows a certain improvement over existing methods.