Neural discovery of abstract inflectional structure

抽象屈折结构的神经发现

• 批准号: 2217554
• 负责人: Micha Elsner
• 金额: $ 33.3万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2217554&HistoricalAwards=false
• 关键词: Neural; discovery; abstract; inflectional; structure

In many languages, words have different forms based on their grammatical role in a sentence. For instance, verbs in Standard English have different forms when the subject is "I/you/we/they" versus "she/he/it" ("I walk", "she walks"). Because many languages have a large number of infrequently used words and many forms of these words, speakers may not be able to memorize all of these forms and must predict some. This project uses computational techniques to measure how predictable the grammatical forms of words are and what information about words (e.g., their sound structure, meaning, or distribution of grammatical forms) contributes to form predictability. The project improves on previous techniques by accounting for cases where unpredictable forms appear in predictable places. For instance, the English verb "sing" has the irregular past tense "sang" and participle "sung". If we know that the verb "give" has an irregular past tense "gave", this (by analogy) increases the chance that the participle is irregular as well, even though the irregular forms involved are different. The project conducts a broad survey of world languages, as well as more focused study of two language families: Romance and Semitic. The project contributes to scientific understanding of the ways in which languages differ from one another and in what respects all human languages must be similar. The project develops practical prediction systems which can be used to improve the ability of technology applications (e.g. automated translators, speech transcription apps, or assistants like Alexa that generate fluent original speech) to handle rare word forms. It also trains a graduate student in advanced computational skills relevant to the technology industry, and the researchers plan to discuss the project in public events designed to engage the local community.This project investigates how inflectional organization facilitates or inhibits the task of predicting previously unobserved inflected forms of words. The extent to which distributional principles shape inflectional organization, facilitate prediction of grammatical forms, and interact with other aspects of morphological organization are typologically not well understood. Drawing on a long history of memory-rich analogical models in morphology, the project develops a computational model which distinguishes between abstract/distributional morphological operations and morphophonological surface form, allowing an independent analysis of the relative difficulty of predicting each dimension. The model is used to conduct a large-scale typological study. This award impacts society in three ways. First, products from this research contribute to computational tools for under-resourced languages, for which inflected form prediction is challenging. All research software created is made publicly available with open-access permissions. Second, the project provides interdisciplinary STEM training. Third, the project supports outreach activities that promote the importance of language diversity and scientific investigation of language via public events.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.

在许多语言中，单词根据它们在句子中的语法作用有不同的形式。例如，当主语是“I/You/we/They”和“he/he/it”(“我走路”、“她走路”)时，标准英语中的动词有不同的形式。由于许多语言有大量不常用的单词和这些单词的许多形式，说话者可能无法记住所有这些形式，必须预测其中的一些。这个项目使用计算技术来衡量单词的语法形式的可预测性，以及关于单词的信息(例如，它们的声音结构、含义或语法形式的分布)对形式可预测性的贡献。该项目对以前的技术进行了改进，解决了不可预测的表单出现在可预测的地方的情况。例如，英语动词“SING”有不规则的过去时“Sang”和分词“Song”。如果我们知道动词“给予”有不规则的过去时“给予”，这(以此类推)增加了分词也是不规则的可能性，即使涉及的不规则形式是不同的。该项目对世界语言进行了广泛的调查，并对罗曼语和闪米特语这两个语系进行了更集中的研究。该项目有助于科学地理解各种语言的不同之处，以及所有人类语言在哪些方面必须相似。该项目开发了实用的预测系统，可用于提高技术应用程序(例如自动翻译器、语音转录应用程序或生成流利的原始语音的Alexa等助手)处理稀有单词形式的能力。它还培训一名研究生与技术行业相关的高级计算技能，研究人员计划在旨在吸引当地社区的公共活动中讨论该项目。该项目调查屈折组织如何促进或抑制预测以前未观察到的单词屈折形式的任务。分布原则在多大程度上塑造了屈折组织，促进了语法形式的预测，并与形态组织的其他方面相互作用，在类型学上还没有得到很好的理解。该项目借鉴了形态学中记忆丰富的类比模型的悠久历史，开发了一种计算模型，该模型区分了抽象/分布的形态运算和形态表面形式，允许对预测每个维度的相对难度进行独立分析。该模型被用于进行大规模的类型学研究。这一奖项在三个方面影响了社会。首先，这项研究的产品为资源不足的语言提供了计算工具，对这些语言来说，屈折形式的预测是具有挑战性的。所有创建的研究软件都是公开提供的，具有开放访问权限。第二，该项目提供跨学科的STEM培训。第三，该项目支持通过公共活动宣传语言多样性的重要性和对语言的科学调查的外联活动。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Analogy in contact: Modeling Maltese plural inflection

接触类比：模拟马耳他语复数变化

• 发表时间: 2023
• 期刊: Proceedings of the Society for Computation in Linguistics
• 作者: Court, Sara;Sims, Andrea D.;Elsner, Micha
• 通讯作者: Elsner, Micha