EAGER: DCL: SaTC: Enabling Interdisciplinary Collaboration: Efficient Human-in-the-Loop Redaction of Language Development Corpora

EAGER：DCL：SaTC：实现跨学科协作：语言开发语料库的高效人机交互编辑

• 批准号: 2210193
• 负责人: Blase Ur
• 金额: $ 30万
• 依托单位: University of Chicago
• 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: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210193&HistoricalAwards=false
• 关键词: EAGER; DCL; SaTC; Enabling; Interdisciplinary

At great effort and expense, and with the cooperation of hundreds of parents, teachers, and children, researchers have collected conversation transcripts to study topics like children's language development. The data most useful for science are longitudinal and naturalistic, such as data collected periodically over time in children's homes. Unfortunately, the longitudinal, naturalistic corpora most likely to advance knowledge may contain information that renders participants identifiable. For this reason, naturalistic corpora are rarely shared with other researchers, hindering science. Sharing requires careful redaction--the removal of potentially identifying information. Currently, naturalistic corpora are often too large for manual redaction, and current automated tools both miss critical redactions and over-redact important information. To enable such data to be shared, this project seeks to develop novel computational methods for redaction.This project's aim is to develop initially automated, human-in-the-loop redaction of identifying information in unstructured text data. First, to better understand key challenges around what aspects of transcripts make participants identifiable, the researchers are conducting interviews with social and behavioral science researchers and members of ethics boards. From these insights, the researchers are developing novel models for predicting what language may need to be redacted and they are designing novel user interactions for leveraging human expertise in redaction decisions. The unique characteristics of conversation transcripts require modeling novel features of language, drawing from natural language processing, psychology, privacy engineering, and linguistics. Because automated methods lack human insights into conversational context for making complex redaction decisions, the researchers are designing user interfaces that summarize how marked language, or tokens, appear longitudinally in transcripts, enabling human coders to quickly make redaction decisions. As a case study, the researchers are applying these techniques to the Language Development Project, a longitudinal corpus of 100 diverse children's development of language. The project is also training students in multidisciplinary research across the computational and social sciences.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.

研究人员花费巨大的精力和费用，在数百名家长、老师和孩子的合作下，收集了对话记录，以研究儿童语言发展等主题。对科学最有用的数据是纵向的和自然的，例如随着时间的推移在儿童家里定期收集的数据。不幸的是，最有可能推进知识的纵向自然主义语料库可能包含使参与者可识别的信息。因此，自然主义语料库很少与其他研究人员共享，这阻碍了科学的发展。共享需要仔细编辑——删除潜在的识别信息。目前，自然语料库通常对于手动编辑来说太大，而当前的自动化工具既会错过关键的编辑，又会过度编辑重要信息。为了使此类数据能够共享，该项目寻求开发新颖的编辑计算方法。该项目的目标是开发最初的自动化、人机交互编辑，以识别非结构化文本数据中的信息。首先，为了更好地了解围绕记录的哪些方面使参与者可识别的关键挑战，研究人员正在对社会和行为科学研究人员以及道德委员会成员进行访谈。根据这些见解，研究人员正在开发新的模型来预测可能需要编辑的语言，并设计新颖的用户交互，以利用人类专业知识进行编辑决策。对话记录的独特特征需要对语言的新颖特征进行建模，借鉴自然语言处理、心理学、隐私工程和语言学的知识。由于自动化方法缺乏人类对对话上下文的洞察力，无法做出复杂的编辑决策，因此研究人员正在设计用户界面，总结标记语言或标记在转录本中纵向出现的方式，使人类编码人员能够快速做出编辑决策。作为一个案例研究，研究人员正在将这些技术应用到语言发展项目中，该项目是一个包含 100 个不同儿童语言发展的纵向语料库。该项目还培训学生进行计算和社会科学的多学科研究。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。