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Improve Neural Machine Translation by Building Word Vector with Part of Speech

Jinyingming Zhang1 , Jin Liu1, *, Xinyue Lin1

1 College of Information Engineering, Shanghai Maritime University, Shanghai, China.

* Corresponding Author: Jin Liu. Email: .

Journal on Artificial Intelligence 2020, 2(2), 79-88.


Neural Machine Translation (NMT) based system is an important technology for translation applications. However, there is plenty of rooms for the improvement of NMT. In the process of NMT, traditional word vector cannot distinguish the same words under different parts of speech (POS). Aiming to alleviate this problem, this paper proposed a new word vector training method based on POS feature. It can efficiently improve the quality of translation by adding POS feature to the training process of word vectors. In the experiments, we conducted extensive experiments to evaluate our methods. The experimental result shows that the proposed method is beneficial to improve the quality of translation from English into Chinese.


Cite This Article

J. Zhang, ,. Jin Liu and X. Lin, "Improve neural machine translation by building word vector with part of speech," Journal on Artificial Intelligence, vol. 2, no.2, pp. 79–88, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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