ITM Web Conf.
Volume 47, 20222022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
|Number of page(s)||9|
|Section||Algorithm Optimization and Application|
|Published online||23 June 2022|
Neural machine translation model combining dependency syntax and LSTM
School of Computer Science and Technology, Harbin University of Science and Technology, Heilongjiang, China
* Corresponding author: firstname.lastname@example.org
For the problem of the lack of linguistic knowledge in the neural machine translation model, which is called Transformer, and the insufficient flexibility of positional encoding, this paper introduces the dependency syntax analysis and the long short-term memory network. The source language syntactic structure information is constructed in the neural machine translation system, and the more accurate position information is obtained by using the memory characteristics of LSTM. Experiments show that using the improved model improves by 1.23 BLEU points in the translation task of the IWSLT14 Chinese-English language pair.
Key words: Neural machine translation / Transformer / Long and short-term memory network / Dependency syntax
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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