Open Access

ARTICLE

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

Jinyingming Zhang¹ , Jin Liu^{1, *}, 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. https://doi.org/10.32604/jai.2020.010476

Received 06 March 2020; Accepted 06 June 2020; Issue published 15 July 2020

Abstract

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.

---

Keywords

---