Machine translation and its evaluation: a study

Subrota Kumar Mondal*, Haoxi Zhang, H. M.Dipu Kabir, Kan Ni, Hong Ning Dai

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)10137–10226
Number of pages90
JournalArtificial Intelligence Review
Issue number9
Early online date19 Feb 2023
Publication statusPublished - Sept 2023

Scopus Subject Areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

User-Defined Keywords

  • Computational linguistics
  • Evaluation methods
  • Natural Language Processing
  • Neural machine translation
  • Statistical machine translation


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