Human-machine Translation Model Evaluation Based on Artificial Intelligence Translation

被引:1
作者
Li, Ruicha [1 ,2 ]
Nawi, Abdullah Mohd [2 ]
Kang, Myoung Sook [2 ]
机构
[1] Xian Fanyi Univ, Sch Translat Studies, Xian, Peoples R China
[2] Univ Teknol Malaysia, Language Acad, Johor Baharu, Malaysia
关键词
Artificial Intelligence; AI-based translation; attention mechanism; Statistical Machine Translation; translation model;
D O I
10.24003/emitter.v11i2.812
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
As artificial intelligence (AI) translation technology advances, big data, cloud computing, and emerging technologies have enhanced the progress of the data industry over the past several decades. Human -machine translation becomes a new interactive mode between humans and machines and plays an essential role in transmitting information. Nevertheless, several translation models have their drawbacks and limitations, such as error rates and inaccuracy, and they are not able to adapt to the various demands of different groups. Taking the AI-based translation model as the research object, this study conducted an analysis of attention mechanisms and relevant technical means, examined the setbacks of conventional translation models, and proposed an AI-based translation model that produced a clear and high quality translation and presented a reference to further perfect AI-based translation models. The values of the manual and automated evaluation have demonstrated that the human -machine translation model improved the mismatchings between texts and contexts and enhanced the accurate and efficient intelligent recognition and expressions. It is set to a score of 1-10 for evaluation comparison with 30 language users as participants, and the achieved 6 points or above is considered effective. The research results suggested that the language fluency score rose from 4.9667 for conventional Statistical Machine Translation to 6.6333 for the AI -based translation model. As a result, the human-machine translation model improved the efficiency, speed, precision, and accuracy of language input to a certain degree, strengthened the correlation between semantic characteristics and intelligent recognition, and pushed the advancement of intelligent recognition. It can provide accurate and high-quality translation for language users and achieve an understanding of natural language input and output and automatic processing.
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页数:15
相关论文
共 25 条
[1]   English teaching practice based on artificial intelligence technology [J].
Bin, Yi ;
Mandal, Durbadal .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2019, 37 (03) :3381-3391
[2]   Deep Learning Approaches for Automatic Drum Transcription [J].
Cahyaningtyas, Zakiya Azizah ;
Purwitasari, Diana ;
Fatichah, Chastine .
EMITTER-INTERNATIONAL JOURNAL OF ENGINEERING TECHNOLOGY, 2023, 11 (01) :21-34
[3]   Towards Integrated Classification Lexicon for Handling Unknown Words in Chinese-Vietnamese Neural Machine Translation [J].
Che, Wanjin ;
Yu, Zhengtao ;
Yu, Zhiqiang ;
Wen, Yonghua ;
Guo, Junjun .
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2020, 19 (03)
[4]   No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications [J].
de Vries, Erik ;
Schoonvelde, Martijn ;
Schumacher, Gijs .
POLITICAL ANALYSIS, 2018, 26 (04) :417-430
[5]  
Fan A, 2021, J MACH LEARN RES, V22
[6]   Design and Development of Educational Robot Teaching Resources Using Artificial Intelligence Technology [J].
Huang, Suo .
INTERNATIONAL JOURNAL OF EMERGING TECHNOLOGIES IN LEARNING, 2021, 16 (05) :116-129
[7]  
Jingyu L., 2019, Chinese Journal of Information Technology, V33, P45
[8]   Neural machine translation in foreign language teaching and learning: a systematic review [J].
Klimova, Blanka ;
Pikhart, Marcel ;
Benites, Alice Delorme ;
Lehr, Caroline ;
Sanchez-Stockhammer, Christina .
EDUCATION AND INFORMATION TECHNOLOGIES, 2023, 28 (01) :663-682
[9]   Artificial Intelligence Based Language Translation Platform [J].
Kolhar, Manjur ;
Alameen, Abdalla .
INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2021, 28 (01)
[10]   Some Translation Studies informed suggestions for further balancing methodologies for machine translation quality evaluation [J].
Kruger, Ralph .
TRANSLATION SPACES, 2022, 11 (02) :213-233