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Optimization of Machine Online Translation System Based on Deep Convolution Neural Network Algorithm.

Juan Zhao1

  • 1School of Foreign Studies, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450046, China.

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Summary
This summary is machine-generated.

This study introduces a deep separable convolution neural network for machine online translation, enhancing efficiency and quality. Pseudo-data learning accelerates system improvement, outperforming Google Translate with sufficient data.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Machine Learning

Background:

  • Machine online translation systems require optimization for improved efficiency and quality.
  • Deep separable convolution neural networks offer a promising approach for complex modeling tasks.

Purpose of the Study:

  • To develop and evaluate a machine online translation model using deep separable convolution neural networks.
  • To assess the effectiveness of pseudo-data learning for accelerating translation system improvement.

Main Methods:

  • Constructed a machine online translation model utilizing a deep separable convolution neural network algorithm.
  • Employed pseudo-data learning for model evaluation and performance enhancement.
  • Designed regression, pseudo-data generation, sorting task, and comparative translation quality experiments.
  • Utilized Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Spearman rank correlation, and delta AVG for performance evaluation.

Main Results:

  • The proposed model demonstrated reduced MAE (2.28%) and RMSE (1.39%) compared to the baseline system.
  • Significant improvements were observed in sorting task performance, with Spearman (132%) and delta AVG (100.7%) increases.
  • Pseudo-data generation for specific tasks requires less data and leads to faster system improvement.
  • Model output quality surpassed Google Translate when using more than 10 instances, achieving over 0.8 similarity.

Conclusions:

  • Deep separable convolution neural networks effectively enhance machine online translation performance.
  • Pseudo-data learning is an efficient strategy for rapid development and optimization of translation systems.
  • The developed model shows competitive translation quality, particularly with sufficient training data.