HCC-Small: Collaborative Research: Statistical Methods for Learning to Diagnose Specific Language Impairment in Bilingual Children

患有特定语言障碍(SLI)的儿童在某些语言技能的获得方面存在延迟，没有听力障碍或其他认知、行为或神经问题的证据。为了诊断单语儿童系统性红斑狼疮，临床医生手头有标准化的测试，如早期语法障碍测试，为不同年龄段的儿童提供定义正常范围的“截止”阈值。然而，由于缺乏标准化测试，缺乏双语临床医生，最重要的是缺乏对双语及其对语言障碍的影响的深入了解，诊断双语儿童SLI要复杂得多。此外，双语儿童经常表现出代码转换的模式，这使得评估任务更具挑战性。PIS在这个项目中的目标是通过开发一种自动化的方法来识别指示SLI的句法模式，从而有助于早期和准确地识别患有SLI的英语-西班牙语双语儿童。目前对双语儿童的鉴别诊断方法主要集中在评估两种语言的语音系统以识别言语障碍儿童，或分析特定语素的错误模式，如冠词性别和数字一致。相比之下，PI的方法不是将分析局限于特定的句法结构，而是专注于适应机器学习(ML)和自然语言处理(NLP)技术，以便他们能够学习区别于其他典型语言发展的模式。公共信息机构将围绕两个核心主题追求目标：双语语篇的自动词性标记(其中他们将调查ML方法的使用，特别是领域适应技术，以结合两种语言的现有语言资源)，以及区分表示SLI的语言使用模式的统计方法(其中，标记者生成的句法信息将用于训练统计模型)。这一方法的直观动机是，SLI双语儿童的语言模式在句法水平和两种语言的交互水平上都会与正常发育儿童的语言模式不同，这些差异将被统计方法捕捉到。广泛的影响：本研究的临床意义是深远的，特别是对于经历SLI的双语儿童的过度识别和识别不足的问题。由于这种诊断的标准涉及识别语言形式、内容和使用的混乱模式，从事代码转换的儿童有被不适当地贴上SLI标签并被安置在特殊教育服务机构的风险。将客观技术应用于诊断过程的能力将成为另一种敏感的评估工具，最终能够更准确地区分表现出语言差异的儿童和经历语言障碍的儿童。对于NLP社区，这项研究将通过开发能够解决涉及跨语言特征、儿童-S自发言语和少量数据的问题的方法来推动最先进的研究。开发的NLP方法将推广到其他临床任务和双语人群。

Children with Specific Language Impairment (SLI) experience a delay in acquisition of certain language skills, with no evidence of hearing impediments or other cognitive, behavioral, or neurological problems. To diagnose monolingual children with SLI, clinicians have at hand standardized tests, such as the Test for Early Grammatical Impairment, that provide "cut-off" thresholds defining the normal range for children of different ages. Diagnosing bilingual children with SLI is far more complicated, however, due to a lack of standardized tests, a lack of bilingual clinicians, and most importantly a lack of a deep understanding of bilingualism and its implications on language disorders. In addition, bilingual children often exhibit code-switching patterns that make the assessment task even more challenging. The PIs' goal in this project is to contribute to the early and accurate identification of English-Spanish bilingual children with SLI, by developing an automated method for discriminating syntactic patterns indicative of SLI. Recent approaches on differential diagnosis of bilingual children are focused either on assessing the phonological systems of both languages to identify children with speech disorders, or on the analysis of error patterns on specific morphemes, such as article gender and number agreement. In contrast, the PIs' approach is not to restrict the analysis to a specific syntactic structure, but rather to focus on adapting Machine Learning (ML) and Natural Language Processing (NLP) techniques so that they can learn the patterns that distinguish an otherwise typical language development. The PIs will pursue the objectives along two core topics: automatic part-of-speech (POS) tagging of bilingual discourse (in which they will investigate the use of ML approaches, in particular domain adaptation techniques, for combining existing linguistic resources on both languages), and statistical methods for discriminating patterns of language use indicative of SLI (in which syntactic information, generated by the tagger will be used to train statistical models). The intuitive motivation for this approach is that the language patterning of bilingual children with SLI will be different from those of typically developing children both at the syntactic level and at the interaction level of the two languages, and these differences will be captured by the statistical methods.Broader Impacts: The clinical implications for this research are far-reaching, particularly regarding the issue of both over- and under-identification of bilingual children experiencing SLI. Because the criteria for this diagnosis involve identification of disordered patterns of language form, content, and use, children who engage in code-switching are at risk of being inappropriately labeled as SLI and placed in special education services. The ability to apply objective technology to the diagnostic process will serve as another sensitive evaluation instrument, eventually allowing for more accurate differentiation of children demonstrating language differences from those experiencing language disorders. For the NLP community, this research will advance the state-of-the-art by developing approaches that can solve problems where the task involves cross-linguistic features, children?s spontaneous speech, and small amounts of data. The NLP methods developed will be generalizable to other clinical tasks and bilingual populations.