Machine translation and its evaluation: a study

Machine translation (namely MT) has been one of the most popular fields in computational linguistics and Artificial Intelligence (AI). As one of the most promising approaches, MT can potentially break the language barrier of people from all over the world. Despite a number of studies in MT, there ar...

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Veröffentlicht in:The Artificial intelligence review Jg. 56; H. 9; S. 10137 - 10226
Hauptverfasser: Mondal, Subrota Kumar, Zhang, Haoxi, Kabir, H. M. Dipu, Ni, Kan, Dai, Hong-Ning
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.09.2023
Springer
Springer Nature B.V
Schlagworte:
Analysis
Artificial Intelligence
Computational linguistics
Computer Science
Evaluation
Language processing
Linguistics
Machine translation
Natural language interfaces
Neural networks
Recurrent
Recurrent neural networks
Syntax
Translation
Translation methods and strategies
Evaluation methods
Computational linguistics
Statistical machine translation
Neural machine translation
Natural Language Processing
ISSN:0269-2821, 1573-7462
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Zusammenfassung:Machine translation (namely MT) has been one of the most popular fields in computational linguistics and Artificial Intelligence (AI). As one of the most promising approaches, MT can potentially break the language barrier of people from all over the world. Despite a number of studies in MT, there are few studies in summarizing and comparing MT methods. To this end, in this paper, we principally focus on presenting the two mainstream MT schemes: statistical machine translation (SMT) and neural machine translation (NMT), including their basic rationales and developments. Meanwhile, the detailed translation models are also presented, such as the word-based model, syntax-based model, and phrase-based model in statistical machine translation. Similarly, approaches in NMT, such as the recurrent neural network-based, attention mechanism-based, and transformer-based models are presented. Last but not least, the evaluation approaches also play an important role in helping developers to improve their methods better in MT. The prevailing machine translation evaluation methodologies are also presented in this article.
Bibliographie:ObjectType-Article-1
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-023-10423-5