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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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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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设计人工智能支持的翻译教育工具:使用SauLTC和LLMs进行并行句子生成的框架.

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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人工智能 (AI) 可以通过自动化任务来增强翻译教育. 这项研究使用了生成预训练变压器 (GPT) 来从现有数据中创建并行句子库,改善了人工智能工具的数据集质量.

关键词:
基于人工智能的翻译技术由人工智能驱动的翻译教育.语料库注释语料库注释教学集体的教学集体.

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科学领域:

  • 计算语言学计算语言学
  • 自然语言处理自然语言处理.
  • 翻译研究 翻译研究

背景情况:

  • 翻译教育 (TE) 是劳动密集型的,人工智能提供了潜在的效率增长.
  • 高质量的数据集对于开发TE中的AI工具至关重要,特别是阿拉伯语.
  • 像沙特学习者翻译集团 (SauLTC) 这样的现有资源并没有被优化为教学目的.

研究的目的:

  • 将 SauLTC 转换为适合人工智能驱动的 TE 的平行句子集.
  • 为了利用生成预训练变压器 (GPT) 模型来增强体.
  • 为教育应用评估人工智能生成的并行句子的质量.

主要方法:

  • 使用生成预训练变压器 (GPT) 来处理和增强SauLTC.
  • 在质量评估中使用与语言不可分的BERT句子嵌入 (LaBSE) 的同位素相似性.
  • 整合了人类评估来验证生成的并行句子.

主要成果:

  • 使用GPT和LaBSE实现了85.2%的相似性得分,优于其他嵌入模型.
  • 证明了AI在生成高质量的并行句子方面的有效性.
  • 在翻译教育中使用数据驱动的解决方案表明了有希望的结果.

结论:

  • 人工智能,特别是GPT,可以有效地解决翻译教育中的数据集限制.
  • 提出的方法成功地将现有的 corpora 转化为有价值的教学资源.
  • 这种方法支持开发更有效和有效的AI驱动的翻译教学工具.