The relationship between the features of Neural Machine Translation (NMT) and university students’ lexical growth

• Main Authors: Salleh, Saiful Daniel, Sahar, Rafidah
• Format: Article
• Language: English; English
• Published: Kulliyyah of Languages & Management(KLM), International Islamic UniversityMalaysia, Edu Hub Pagoh, 84600 Muar, Johor Darul Takzim. 2022
• Subjects: PE English
• Online Access: http://irep.iium.edu.my/99867/1/IYSJL-Vol-5-No-1.pdf; http://irep.iium.edu.my/99867/3/99867_The%20relationship%20between%20the%20features%20of%20Neural%20Machine%20Translation%20%28NMT%29.pdf; http://irep.iium.edu.my/99867/; https://kulliyyah.iium.edu.my/klm/journals/

The rise of technology from the previous years has contributed to the improvement of Neural Machine Translation (NMT). In this context, machine translation is a process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish). It is well established that the most frequent users of NMT are university students since they are dealing with languages in their pedagogy. This study explored the advantages of NMT among students in terms of their lexical development. Specifically, the study examined how features of NMT namely tangibility, assurance, and reliability were related to the lexical growth of university students. This quantitative study was conducted on 80 university students studying at Malaysian public universities through convenience sampling. Data were collected by a valid and reliable questionnaire that was distributed online. Data sets collected were analysed by SPSS-17 software. The findings indicated that tangibility, assurance, and reliability of the NMT do not have a positive relationship with the students’ lexical growth.