The Machine Translation Model

DOI

10.2991/978-2-494069-89-3_247How to use a DOI?

Keywords

Translation model; Source language; Target language; Monolingual; Multilingual

Abstract

Machine Translation was invented in the late-20^th century, when the first IBM model could automatically translate Russian sentences into English. Proceeding to the 21^st century, linguists have invented new types of Machine Translation models and improved on them by altering and adding parts. Of all the various types of Machine Translation models, this paper will mainly focus on Statistical Machine Translation models, which put into play the statistical data with their calculation of the probabilities of the words and phrases, and Neural Machine Translation models, which are built by the stacking and connecting of neural layers through encoders and decoders, by comparing the benefits and flaws of the types within the specific Machine Translation models. A specific translation will be mentioned using the Neural Machine Translation models, revealing the flaws within its translation. From the flaws of Neural Machine Translation models, this paper will also examine attempts to improve them by solving existing problems. This paper will build a basic understanding of machine translation models and possibly inspire future experiments and extensions to improve the translation models both mentioned and not mentioned in this paper.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ziyuan Zhao
PY  - 2022
DA  - 2022/12/30
TI  - The Machine Translation Model
BT  - Proceedings of the 2022 5th International Conference on Humanities Education and Social Sciences (ICHESS 2022)
PB  - Atlantis Press
SP  - 2153
EP  - 2160
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-494069-89-3_247
DO  - 10.2991/978-2-494069-89-3_247
ID  - Zhao2022
ER  -