人工智能在为后代保护语言多样性方面的潜力

T. Ermolova, Natal’ya Vasil’evna Savitskaya, O. Dedova, A. V. Guzova
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摘要

导言。保护世界文化遗产和语言多样性这一重要且仅部分解决的问题,可以通过使用现代技术,特别是人工智能(AI),获得更多的解决途径。本文旨在分析有关利用人工智能技术保护语言多样性的可能性的最新研究数据,并在考虑到决定人工智能发展的语言变革的情况下评估其有效性。材料与方法。本综述旨在分析学术文献中关于人工智能在保护语言多样性方面的潜力以及在应用于稀有和濒危语言方面的现有局限性的数据。结果。通过对研究问题相关数据的搜索发现,将人工智能技术作为执行保护语言多样性任务的工具仍主要是媒体和公共领域的讨论主题,在学术文献中的代表性极低。与人工智能潜力部分相关的挑战包括许多人工智能系统的语言偏见,这种偏见植根于用于训练这些系统的数据中,会进一步巩固某些族群之间的社会和语言不平等,并给借助人工智能建立教授这些语言的教育模式造成困难。作者认为,可以训练人工智能系统识别和分析研究不足或濒危语言的语言模式,包括为面临失去交流语言风险的小族群建立母语教育模式。有证据表明,人工智能可以重新创造失传的语言。利用人工智能保护语言多样性的一个主要挑战是缺乏有关许多语言的数据,而这些语言在媒体领域很少使用或濒临灭绝。结论最后,作者得出结论,人工智能技术被认为在保护语言多样性方面大有可为。
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The potential of artificial intelligence in preserving linguistic diversity for future generations
Introduction. The significant and only partially solved problem of preserving cultural heritage and linguistic diversity in the world can obtain additional ways of solving by means of using modern technologies, in particular, the Artificial Intelligence (AI). The purpose of this article is to analyze up-to-date research data on the possibility of preserving linguistic diversity by means of Artificial Intelligence technologies and evaluate their effectiveness taking into account linguistic transformations determining the development of AI. Materials and Methods. This review is aimed at analyzing the data available in scholarly literature on the potential of artificial intelligence in preserving linguistic diversity and the existing limitations in the case of its application to rare and endangered languages. Results. The search for data on the research problem revealed that using AI technologies as tools for implementing the task of preserving linguistic diversity is still the subject of discussion mainly in the media and public spheres and is extremely poorly represented in academic literature. Among the challenges partially related to the potential of AI is the linguistic bias of many AI systems, which is rooted in the data used to train them, which can further entrench social and linguistic inequalities among certain ethnic groups and create difficulties in building educational models for teaching these languages with the help of AI. The authors argue that AI systems can be trained to recognize and analyze linguistic patterns of languages that have been understudied or are endangered, including building educational models in native languages for small ethnic groups at risk of losing the language of communication. There is evidence that AI can recreate lost languages. A major challenge in using AI to preserve linguistic diversity is the lack of data on many languages that are rarely used in the media space or are endangered. Conclusions. Finally, the authors conclude that AI technologies are recognized as promising for preserving linguistic diversity.
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