CAREER: Modeling Language Evolution via Deep Probabilistic Factorization

职业：通过深度概率分解建模语言演化

• 批准号: 2146151
• 负责人: Taylor Berg-Kirkpatrick
• 金额: $ 60万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146151&HistoricalAwards=false
• 关键词: CAREER; Modeling; Language; Evolution; via

The broad diversity of contemporary and historical languages, dialects, and writing systems presents a daunting challenge for artificial intelligence (AI) systems that must process language data (e.g. systems that automatically recognize handwriting or attempt to translate from one language to another). However, within this great diversity, there are strong patterns of regularity. Historical linguists have shown that many aspects of language evolve over time in accordance with regular patterns of change, including spoken language, spelling, and even the visual appearance of symbols. This project aims to develop novel AI frameworks that can better understand language diversity by automatically analyzing large and diverse datasets consisting of many languages, dialects, and writing systems. The project will result in a collection of new AI systems that track how visual and textual aspects of language evolve over time in order to (1) provide better understanding of how languages change and develop and (2) make downstream AI systems more robust to language diversity. Finally, this research will also support interdisciplinary training of a diverse set of graduate students at the University of California San Diego, as well as the development of interdisciplinary educational modules for high school students interested in AI. This CAREER project will develop a novel computational framework that combines methods from matrix and tensor factorization with deep generative modeling techniques to support analysis of language evolution over a broad range of languages, dialects, and writing systems. The project will create a learning paradigm that (1) incorporates prior phylogenetic knowledge of language history as structured priors, (2) supports efficient approximate inference of historical language forms using neural decoders, (3) is easily portable to a variety of linguistic domains and levels of language representation, and (4) directly analyzes primary data (e.g. images of signs) rather than manually-curated feature lists. Further, the framework will generalize across both visual and textual modalities, allowing for study of the multi-modal nature language evolution -- e.g. scripts evolve through visual change, cognates through phonetic or orthographic change -- and potentially laying the groundwork for future work investigating how script and dialect co-evolve or cultural evolution studies of spoken audio. Finally, the outcomes of each of several applied studies may lead to new evidence for specific historical and paleographic hypotheses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

当代和历史语言、方言和书写系统的广泛多样性对必须处理语言数据的人工智能（AI）系统（例如自动识别手写或尝试从一种语言翻译为另一种语言的系统）提出了严峻的挑战。然而，在这种巨大的多样性中，存在着强烈的规律性模式。历史语言学家已经表明，语言的许多方面随着时间的推移而演变，包括口语，拼写，甚至符号的视觉外观。该项目旨在开发新型人工智能框架，通过自动分析由多种语言、方言和书写系统组成的大型多样化数据集，更好地理解语言多样性。该项目将产生一系列新的人工智能系统，跟踪语言的视觉和文本方面如何随着时间的推移而演变，以便（1）更好地理解语言如何变化和发展，（2）使下游人工智能系统对语言多样性更加强大。最后，这项研究还将支持加州圣地亚哥大学对不同研究生的跨学科培训，以及为对人工智能感兴趣的高中生开发跨学科教育模块。这个CAREER项目将开发一个新的计算框架，将矩阵和张量因式分解的方法与深度生成建模技术相结合，以支持对各种语言，方言和书写系统的语言进化进行分析。该项目将创建一个学习范式，（1）将语言历史的先验系统发育知识作为结构化先验，（2）支持使用神经解码器对历史语言形式进行有效的近似推理，（3）易于移植到各种语言领域和语言表示水平，以及（4）直接分析原始数据（例如，标志的图像）而不是人工策划的特征列表。此外，该框架将概括视觉和文本形式，允许研究多模态的自然语言演变-例如，脚本通过视觉变化演变，同源词通过语音或拼写变化-并可能为未来的工作奠定基础调查脚本和方言如何共同演变或口语音频的文化演变研究。最后，每一个应用研究的结果可能会导致特定的历史和古地理假说的新证据。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。