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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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Mondal, Subrota Kumar Kabir, H. M. Dipu Dai, Hong-Ning Zhang, Haoxi Ni, Kan
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Snippet	Machine translation (namely MT) has been one of the most popular fields in computational linguistics and Artificial Intelligence (AI). As one of the most...
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SubjectTerms	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
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Title	Machine translation and its evaluation: a study
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