Collaborative Research: RI: Medium: From Acoustic Signal to Morphosyntactic Analysis in One End-to-End Neural System

当今世界上大约有7000种语言，但这个数字正在急剧下降。即使是目前有成千上万使用者的许多语言，也可能在一代人的时间内不再被使用。对于这些语言的使用者来说，这代表着文化和语言遗产的悲剧性损失，而文化和语言遗产是他们社会身份的重要支柱。每一种语言都承载着作为一种人类行为现象的语言的不可替代的资料——语言变化的界限以及语言结构和发展的模式。语言学家和语言活动家目前正在努力快速、全面地记录尽可能多的语言。不幸的是，即使一种语言逐渐淡出人们的使用，文档也能确保它的数据仍可用于未来的文化或科学分析。该项目使用自然语言处理和机器学习的工具部分自动化语言文档的过程。它与类似项目的不同之处在于，它使用一个集成系统来处理语音和单词结构，而不是使用两个或多个独立的组件。在母语学者的合作下，研究人员正在将他们的方法应用于四种语言：高原普ebla Nahuatl， Yoloxóchitl米斯特克语，圣佩德罗AmuzgosAmuzgo语和北坡Iñupiaq语。提出的研究将通过引入端到端系统，将语音作为输入，并产生行间注释作为输出，从而极大地改变自动形态句法和形态音素分析的格局。研究小组建议建立一个端到端的系统，一个单一的神经网络，使用母语语言学家产生的少量标记数据，可以直接将录制的语音转换为分析的文本，产生四种输出：(1)表面转录，(2)表面形式的形态分割，(3)每个语素的基础或规范形式，以及(4)每个语素的注释或标准化标签。提出的单端到端神经网络代表了将上述四种任务集成到单个神经网络中的首次尝试，避免了之前尝试创建管道时遇到的错误传播问题，并减轻了最终用户技术的复杂性。研究人员还提出了将语言知识整合到神经网络中的创新方法，包括使用可微加权有限状态换能器，该换能器由迭代自我训练架构独立驱动。这种迭代自我训练的方法，就其本身而言，将代表机器学习的一种进步——一种增加单词和语素权重的新算法。该研究也对计算形态学做出了重大贡献。它包括一个简单而富有表现力的修改现有的分割和修饰方案，特别是对于不连续语素的表示。此外，该提案通过系统地解决衍生和屈折，扩展了流行的形态学分析方法（例如，UniMorph）。这个建议解决了先前的工作没有解决的重复和名词合并的注释问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

There are approximately 7,000 languages in the world today, but this number is declining precipitously.Even many languages that currently have thousands upon thousands of speakers are likely to fall outof use within a generation. For the speakers of these languages, this represents a tragic loss of culturaland linguistic heritage, which are important anchors of their social identity. Each language also carriesirreplaceable data about language as a phenomenon of human behavior—the limits of its variation andthe patterns in its structure and development. Linguists and language activists are currently working toquickly and comprehensively document as many languages as possible. In the unfortunate event that alanguage fades from use, documentation ensures that its data will remain available for future cultural orscientific analysis. This project partially automates the process of language documentation using toolsfrom Natural Language Processing and Machine Learning. It differs from similar projects in using oneintegrated system to process the sounds of speech and the structure of words, instead of using two ormore separate components. With the collaboration of native speaker scholars, the researchers are applyingtheir methodology to four languages: Highland Puebla Nahuatl, Yoloxóchitl Mixtec, San Pedro AmuzgosAmuzgo, and North Slope Iñupiaq.The proposed research will dramatically transform the landscape of automatic morphosyntactic andmorphophonological analysis by introducing an end-to-end system that consumes speech as an input andproduces interlinear annotations as an output. The research team proposes to build an end-to-end system,a single neural net that, with small amounts of labeled data produced by native speaker linguists, candirectly convert recorded speech to analyzed text, producing four outputs: (1) surface transcription, (2)morphological segmentation of surface forms, (3) an underlying or canonical form for each morpheme,and (4) a gloss or standardized label for each morpheme. The proposed single end-to-end neural networkrepresents the first attempt to integrate the four aforementioned tasks into a single neural network, avoidingthe error-propagation problems that have plagued earlier attempts at creating a pipeline and mitigating thecomplexity of the technology for end-users. The researchers also propose innovative ways to incorporate linguisticknowledge into neural networks, including the use of differentiable weighted finite-state transducers,which are independently motivated by an iterative self-training architecture. This approach to iterative self training,in its own right, will represent an advance in machine learning — a new algorithm for upweightingwords and morphemes. The research also makes significant contributions to computational morphology.It includes a simple but expressive modification to existing schemes for segmentation and glossing, specificallyfor the representation of discontinuous morphemes. Furthermore, the proposal extends popularapproaches to morphological analysis (e.g., UniMorph) by systematically addressing derivation as well asinflection. This proposal addresses glossing of reduplication and noun-incorporation, which earlier workhas not.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.