A Novel English Translation Model in Complex Environments Using Two-Stream Convolutional Neural Networks.

Area of Science:

• Educational Technology
• Computational Linguistics
• Second Language Acquisition

Background:

• Traditional English language pedagogy often prioritizes listening, reading, and writing skills over translation.
• The evolving demands of English proficiency assessments, particularly in China, highlight the necessity of strong translation capabilities.
• Neglecting translation skills can hinder comprehensive language mastery and academic success.

Purpose of the Study:

• To investigate the impact of a two-stream convolutional neural network-based English translation simulation model on students' translation skills.
• To provide empirical evidence supporting the integration of translation practice in English language teaching.

Main Methods:

• Implementation of an English translation simulation model utilizing a two-stream convolutional neural network architecture.
• Experimental evaluation of the model's effectiveness through pre- and post-intervention assessments of student performance.
• Analysis of student performance data in vocabulary, grammar, reading, and translation.

Main Results:

• Students using the simulation model demonstrated significant improvements in English translation.
• Vocabulary and grammar skills showed high success rates (90% passing, 57% excellent).
• Reading comprehension also improved, with passing and excellent rates of 69% and 8%, respectively, indicating a positive effect on overall language skills.

Conclusions:

• The two-stream convolutional neural network-based translation simulation model effectively enhances students' English translation abilities.
• Integrating such simulation models into English language classrooms is recommended to address the growing importance of translation skills.
• This approach offers a promising avenue for improving language education and meeting assessment requirements.