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Lesson: Translation
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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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Translocation of proteins across membranes is an ancient process that occurs even in bacteria and archaebacteria. In fact, the components of the translocation machinery are still conserved between prokaryotes and eukaryotes.
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在主题分类中对静态和上下文词嵌入的反向翻译效应

Dávid Držík1, Lívia Kelebercová1

  • 1Department of Informatics, Faculty of Natural Science and Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovakia.

PloS one
|August 29, 2025
PubMed
概括

逆向翻译显著改善了静态词嵌入的主题分类,但对于像RoBERTa这样的高级上下文模型来说,

科学领域:

  • 自然语言处理
  • 机器学习

背景情况:

  • 主题分类对于组织文本数据至关重要.
  • 数据增强技术,如反向翻译,可以提高模型的性能.
  • 在不同类型的词嵌入中评估这些技术是必不可少的.

研究的目的:

  • 评估反向翻译对主题分类表现的影响.
  • 将其对静态词向量 (FastText) 与上下文嵌入 (RoBERTa) 的有效性进行比较.
  • 确定低资源语言和各种分类算法的好处.

主要方法:

  • 实验使用了物流回归,SVM,随机森林和RNN-LSTM分类器.
  • 通过六种语言的逆向翻译来增强数据.
  • 使用F1评分评估性能,比较原始和增强数据.

主要成果:

  • 反向翻译对静态嵌入的F1得分进行了持续的改进 (对于随机森林,高达2.80%).
  • 对于静态嵌入的RNN-LSTM,改进较小,并且往往没有统计学意义.
  • 反向翻译对RoBERTa的上下文嵌入的影响很小,没有显著的F1得分增长.

结论:

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  • 逆向翻译是对主题分类中的静态词嵌入的有价值的数据增强策略,特别是在低资源语言中.
  • 像RoBERTa这样的现代上下文模型表现出更低的依赖外部增强的高性能.
  • 对于高级上下文感知模型来说,反向翻译的实用性是有限的.