Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation

Naeem, Muhammad; Siddique, Abu Bakar; Ali, Raja Hashim; Arshad, Usama; Abideen, Zain ul; Ali Khan, Talha; Shah, Muhammad Huzaifa; Zeeshan Ijaz, Ali; Ali, Nisar

doi:10.1109/fit60620.2023.00048

2023 International Conference on Frontiers of Information Technology (FIT) 2023

DOI: 10.1109/fit60620.2023.00048

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation

Muhammad Naeem,

Abu Bakar Siddique,

Raja Hashim Ali

et al.

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2024

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Improved Urdu-English Neural Machine Translation with a fully Convolutional Neural Network Encoder

Israr,

Shahzad,

Anwar

2024

Int. j. math. eng. manag. sci.

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Neural machine translation (NMT) approaches driven by artificial intelligence (AI) has gained more and more attention in recent years, mainly due to their simplicity yet state-of-the-art performance. Despite NMT models with attention mechanism relying heavily on the accessibility of substantial parallel corpora, they have demonstrated efficacy even for languages with limited linguistic resources. The convolutional neural network (CNN) is frequently employed in tasks involving visual and speech recognition. Implementing CNN for MT is still challenging compared to the predominant approaches. Recent research has shown that the CNN-based NMT model cannot capture long-term dependencies present in the source sentence. The CNN-based model can only capture the word dependencies within the width of its filters. This unnatural character often causes a worse performance for CNN-based NMT than the RNN-based NMT models. This study introduces a simple method to improve neural translation of a low-resource language, specifically Urdu-English (UR-EN). In this paper, we use a Fully Convolutional Neural Network (FConv-NN) based NMT architecture to create a powerful MT encoder for UR-EN translation that can capture the long dependency of words in a sentence. Although the model is quite simple, it yields strong empirical results. Experimental results show that the FConv-NN model consistently outperforms the traditional CNN-based model with filters. On the Urdu-English Dataset, the FConv-NN model produces translation with a gain of 18.42 BLEU points. Moreover, the quantitative and comparative analysis shows that in a low-resource setting, FConv-NN-based NMT outperforms conventional CNN-based NMT models.

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Improved Urdu-English Neural Machine Translation with a fully Convolutional Neural Network Encoder

Israr,

Shahzad,

Anwar

2024

Int. j. math. eng. manag. sci.

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Performance Evaluation of Popular Deep Neural Networks for Neural Machine Translation

Cited by 1 publication

References 23 publications

Improved Urdu-English Neural Machine Translation with a fully Convolutional Neural Network Encoder

Improved Urdu-English Neural Machine Translation with a fully Convolutional Neural Network Encoder

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