Open Access iconOpen Access

ARTICLE

crossmark

Neural Machine Translation Models with Attention-Based Dropout Layer

Huma Israr1,*, Safdar Abbas Khan1, Muhammad Ali Tahir1, Muhammad Khuram Shahzad1, Muneer Ahmad1, Jasni Mohamad Zain2,*

1 School of Electrical Engineering and Computer Science (SEECS), National University of Science and Technology, Islamabad, Pakistan
2 Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Kompleks Al-Khawarizmi, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia

* Corresponding Authors: Huma Israr. Email: email; Jasni Mohamad Zain. Email: email

Computers, Materials & Continua 2023, 75(2), 2981-3009. https://doi.org/10.32604/cmc.2023.035814

Abstract

In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen data. The trained models yielded competitive results by achieving 62.6% and 61% accuracy and 49.67 and 47.14 BLEU scores, respectively. From a qualitative perspective, the translation of the test sets was examined manually, and it was observed that trained models tend to produce repetitive output more frequently. The attention score produced by Bi-RNN and LSTM produced clear alignment, while GRU showed incorrect translation for words, poor alignment and lack of a clear structure. Therefore, we considered refining the attention-based models by defining an additional attention-based dropout layer. Attention dropout fixes alignment errors and minimizes translation errors at the word level. After empirical demonstration and comparison with their counterparts, we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score. The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well. We empirically concluded that adding an attention-based dropout layer helps improve GRU, SRU, and Transformer translation and is considerably more efficient in translation quality and speed.

Keywords


Cite This Article

APA Style
Israr, H., Khan, S.A., Tahir, M.A., Shahzad, M.K., Ahmad, M. et al. (2023). Neural machine translation models with attention-based dropout layer. Computers, Materials & Continua, 75(2), 2981-3009. https://doi.org/10.32604/cmc.2023.035814
Vancouver Style
Israr H, Khan SA, Tahir MA, Shahzad MK, Ahmad M, Zain JM. Neural machine translation models with attention-based dropout layer. Comput Mater Contin. 2023;75(2):2981-3009 https://doi.org/10.32604/cmc.2023.035814
IEEE Style
H. Israr, S.A. Khan, M.A. Tahir, M.K. Shahzad, M. Ahmad, and J.M. Zain "Neural Machine Translation Models with Attention-Based Dropout Layer," Comput. Mater. Contin., vol. 75, no. 2, pp. 2981-3009. 2023. https://doi.org/10.32604/cmc.2023.035814



cc 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.
  • 899

    View

  • 520

    Download

  • 0

    Like

Share Link