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

在数百名家长、老师和孩子的合作下，研究人员不辞辛劳地收集了对话记录，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主义的，比如儿童之家随着时间的推移定期收集的数据。不幸的是，最有可能推进知识进步的纵向、自然主义语料库可能包含使参与者可识别的信息。出于这个原因，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。共享需要仔细的密文--删除可能识别身份的信息。目前，自然主义语料库往往太大，无法进行手动编辑，而当前的自动化工具既错过了关键的编辑，又过度编辑了重要的信息。为了使这些数据能够被共享，该项目寻求开发新的编辑计算方法。该项目的目标是开发最初自动化的、人在循环中识别非结构化文本数据中的信息的编辑。首先，为了更好地了解关于成绩单哪些方面的关键挑战，研究人员正在对社会和行为科学研究人员以及伦理委员会成员进行采访。根据这些见解，研究人员正在开发新的模型来预测哪些语言可能需要编辑，他们正在设计新的用户交互，以便在编辑决策中利用人类的专业知识。会话记录的独特特征要求从自然语言处理、心理学、隐私工程学和语言学的角度出发，对语言的新特征进行建模。由于自动化方法缺乏对对话环境的人类洞察力，无法做出复杂的密文决策，研究人员正在设计用户界面，总结标记语言或标记在文字记录中纵向出现的方式，使人类编码员能够快速做出密文决策。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，这是一个由100名不同儿童组成的纵向语料库。该项目还培训学生在计算和社会科学领域进行多学科研究。这一奖项反映了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。

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.