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A Cooperative Lightweight Translation Algorithm Combined with Sparse-ReLU.

Xintao Xu^1,2, Yi Liu², Gang Chen²

¹School of Microelectronics, University of Science and Technology of China, Hefei, China.

Computational Intelligence and Neuroscience

|June 7, 2022

Summary

This study introduces Sparse-ReLU to enhance machine translation models. The new method improves accuracy and significantly reduces model size for better hardware deployment.

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Area of Science:

Natural Language Processing (NLP)
Deep Learning Architectures

Background:

Transformer-based machine translation models face hardware deployment challenges due to large parameter counts and low sparsity.
Lightweight machine translation networks require accuracy improvements.

Purpose of the Study:

To improve the parametric sparsity and accuracy of machine translation algorithms.
To facilitate the hardware deployment of Transformer-based models.

Main Methods:

Designed a novel activation function, Sparse-ReLU, to enhance weight and feature map sparsity.
Developed a cooperative processing scheme integrating Convolutional Neural Networks (CNN) and Transformer architectures, utilizing Sparse-ReLU.

Main Results:

Achieved a 2.32% BLEU score improvement in prediction accuracy.
Reduced model parameters by 23%.
Increased inference model sparsity by over 50%.

Conclusions:

The combination of Transformer, CNN, and Sparse-ReLU effectively addresses hardware deployment limitations.
The proposed method offers a promising solution for accurate and efficient machine translation.