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，单词基于其在句子中的语法角色而具有不同的形式。例如，当主题为“ i/you/we/she“ v she/he/it”（“我走路”，“她走路”）时，标准英语的动词具有不同的形式。由于许多语言都有大量使用的单词和许多形式的单词，因此说话者可能无法记住所有这些形式，并且必须预测一些形式。该项目使用计算技术来衡量单词的语法形式是多么可预测的，以及有关单词的信息（例如它们的声音结构，含义或语法形式的分布）有助于形成可预测性。该项目通过考虑在可预测的地方出现不可预测形式的情况来改进以前的技术。例如，英语动词“ sing”具有不规则的过去时“ sang”和分词“ Sung”。如果我们知道动词“给出”具有不规则的过去时“给出”，那么（类比）这会增加分词不规则的机会，即使涉及的不规则形式不同。该项目对世界语言进行了广泛的调查，以及对两个语言系列的更加集中研究：浪漫和闪族人。 该项目有助于科学理解语言彼此不同的方式以及所有人类语言都必须相似的方式。 该项目开发了实际的预测系统，可用于提高技术应用程序的能力（例如自动翻译人员，语音转录应用程序，或像Alexa这样的助手来产生流利的原始语音）来处理稀有单词形式。它还培训了一名与技术行业相关的高级计算技能的研究生，研究人员计划在旨在吸引当地社区的公共事件中讨论该项目。该项目调查了拐点组织如何促进或抑制预测先前未观察到的拐点的单词形式的任务。 分布原理塑造拐点组织的程度，促进语法形式的预测以及与形态组织的其他方面相互作用的程度在类型上尚未很好地理解。该项目借鉴了形态学富含记忆的类似模型的悠久历史，开发了一个计算模型，该模型区分了抽象/分布形态学作业和形态学表面形式，从而使预测每个维度的相对难度进行了独立的分析。该模型用于进行大规模的类型学研究。 该奖项以三种方式影响社会。首先，这项研究的产品有助于用于资源不足的语言的计算工具，对于该语言而言，易转的形式预测是具有挑战性的。所有创建的研究软件均可公开获得开放式权限权限。其次，该项目提供跨学科的STEM培训。第三，该项目支持推广活动，这些活动通过公共活动来促进语言多样性和科学研究的重要性。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来提供支持。

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