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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Designing AI-powered translation education tools: a framework for parallel sentence generation using SauLTC and LLMs.

Moneerh Aleedy1,2, Fatma Alshihri3, Souham Meshoul1

  • 1Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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Summary

Artificial intelligence (AI) can enhance translation education by automating tasks. This study used Generative Pre-trained Transformer (GPT) to create a parallel sentence corpus from existing data, improving dataset quality for AI tools.

Keywords:
AI-based translation technologyAI-powered translation educationCorpus annotationDidactic corpus

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Area of Science:

  • Computational linguistics
  • Natural Language Processing
  • Translation Studies

Background:

  • Translation education (TE) is labor-intensive, with AI offering potential efficiency gains.
  • High-quality datasets are crucial for developing AI tools in TE, particularly for Arabic.
  • Existing resources like the Saudi Learner Translation Corpus (SauLTC) are not optimized for didactic purposes.

Purpose of the Study:

  • To transform the SauLTC into a parallel sentence corpus suitable for AI-driven TE.
  • To leverage Generative Pre-trained Transformer (GPT) models for corpus enhancement.
  • To evaluate the quality of AI-generated parallel sentences for educational applications.

Main Methods:

  • Utilized Generative Pre-trained Transformer (GPT) to process and augment the SauLTC.
  • Employed cosine similarity with Language-agnostic BERT Sentence Embedding (LaBSE) for quality assessment.
  • Incorporated human evaluation to validate the generated parallel sentences.

Main Results:

  • Achieved an 85.2% similarity score using GPT and LaBSE, outperforming other embedding models.
  • Demonstrated the effectiveness of AI in generating high-quality parallel sentences.
  • Indicated promising results for data-driven solutions in translation education.

Conclusions:

  • AI, specifically GPT, can effectively address dataset limitations in translation education.
  • The proposed method successfully transforms existing corpora into valuable didactic resources.
  • This approach supports the development of more efficient and effective AI-powered translation teaching tools.