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Academic Journal of Humanities & Social Sciences, 2023, 6(21); doi: 10.25236/AJHSS.2023.062102.

An Analysis of the Factors of Mistranslation in Statistical Machine Translation—Taking Prose Text Translation as Example

Author(s)

Yon Jee Kwun (Yang Yikun)1, Yon Jee Han (Yang Yihan)2

Corresponding Author:
Yon Jee Kwun (Yang Yikun)
Affiliation(s)

1Foreign Language School, Gannan Normal University, Ganzhou, China

2School of Event and Communication, Suibe, Shanghai, China

Abstract

This article analyzes the factors of mistranslation in Statistical Machine Translation (STM) by taking prose translation as an example. First, it reviews the basics of STM and prose translation, and then discusses the common mistranslation types in STM, including lexical, syntactic, semantic, and pragmatic mistakes. Next, it identifies possible factors contributing to these mistranslations, including the quality of training data, the selected translation model, and the limitations of machine learning algorithms. Finally, it proposes some possible solutions to reduce mistranslation in STM, such as improving the quality of training data, selecting more appropriate translation models, and exploring new mistranslation detection techniques. The analysis presented in this article provides valuable insights into the challenges and opportunities in STM, and helps improve the accuracy and quality of machine translation.

Keywords

Statistical Machine Translation; Prose Text Translation; Mistranslation

Cite This Paper

Yon Jee Kwun (Yang Yikun), Yon Jee Han (Yang Yihan). An Analysis of the Factors of Mistranslation in Statistical Machine Translation—Taking Prose Text Translation as Example. Academic Journal of Humanities & Social Sciences (2023) Vol. 6, Issue 21: 6-12. https://doi.org/10.25236/AJHSS.2023.062102.

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