TY - JOUR
T1 - Machine translation and its evaluation
T2 - a study
AU - Mondal, Subrota Kumar
AU - Zhang, Haoxi
AU - Kabir, H. M.Dipu
AU - Ni, Kan
AU - Dai, Hong Ning
N1 - Funding Information:
The authors would like to thank the anonymous reviewers for their quality reviews and suggestions. This work was supported in part by The Science and Technology Development Fund of Macao, Macao SAR, China under Grant 0033/2022/ITP and in part by The Faculty Research Grant Projects of Macau University of Science and Technology, Macao SAR, China under Grant FRG-22-020-FI.
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature B.V.
PY - 2023/9
Y1 - 2023/9
N2 - 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.
AB - 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.
KW - Computational linguistics
KW - Evaluation methods
KW - Natural Language Processing
KW - Neural machine translation
KW - Statistical machine translation
UR - http://www.scopus.com/inward/record.url?scp=85148377947&partnerID=8YFLogxK
U2 - 10.1007/s10462-023-10423-5
DO - 10.1007/s10462-023-10423-5
M3 - Journal article
AN - SCOPUS:85148377947
SN - 0269-2821
VL - 56
SP - 10137
EP - 10226
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 9
ER -