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Collaborative Research: Automatic Text-Simplification and Reading-Assistance to Support Self-Directed Learning by Deaf and Hard-of-Hearing Computing Workers

协作研究：自动文本简化和阅读辅助，支持聋哑和听力障碍计算工作者的自主学习

基本信息

批准号：
2055699
负责人：
Wei Xu
金额：
$ 28.89万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2055699&HistoricalAwards=false
关键词：
Collaborative Research Automatic Text Simplification

项目摘要

While there is a shortage of computing and IT professionals in the U.S., there is underrepresentation of people who are Deaf and Hard of Hearing (DHH) in such careers. There is great diversity in the English reading skills among DHH Americans, who face challenges in the IT industry where regular upskilling is required throughout the working career. This interdisciplinary research will focus on studying automatic text-simplification technologies as reading assistance to help DHH individuals keep up with rapidly changing technologies through self-directed learning, outside of a formal classroom setting. The research team consists of experts in natural language processing (NLP), human-computer interaction (HCI), accessibility, and Deaf STEM education research. The resources and technologies developed for this project will be adaptable to other languages and text domains (e.g. medical information for lay readers), benefiting a wide range of populations (e.g. children, non-native speakers, people with reading disabilities).Prior text-simplification research for specific user groups focused on preliminary data collection or classical NLP methods, and little HCI research has explored design options for reading-assistance systems. This project will fill this critical gap in research and will investigate the learning and reading-assistance needs of upskilling DHH computing workers, design parameters influencing usability of reading-assistance tools, new training data and methods for text simplification tailored to specific reader groups and technical genres, automatic and human-based evaluation methods, and the impact of such tools on heutagogical learning. The methods will include interview and survey research with DHH computing workers, prototyping and testing of design variations, creation of parallel simplification corpora, readability annotation of lexicon and texts by DHH individuals, NLP research on domain adaptation and syntax-based neural machine translation for text simplification, and observation of real-world deployment of a prototype with DHH students and recent graduates from computing-related programs at the National Technical Institute for the Deaf (NTID).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.

虽然美国的计算机和IT专业人员短缺，但在这些职业中，聋人和重听人（DHH）的代表性不足。DHH美国人的英语阅读技能差异很大，他们在IT行业面临挑战，在整个职业生涯中都需要定期提高技能。这项跨学科研究将重点研究自动文本简化技术，作为阅读辅助，帮助DHH个体在正式课堂环境之外通过自主学习跟上快速变化的技术。该研究小组由自然语言处理（NLP）、人机交互（HCI）、无障碍、聋人STEM教育研究等领域的专家组成。为这一项目开发的资源和技术将适用于其他语言和文本领域（例如为外行读者提供的医疗信息），使广泛的人群（例如儿童、非母语人士、阅读障碍者）受益。先前针对特定用户群体的文本简化研究主要集中在初步数据收集或经典的自然语言处理方法上，很少有HCI研究探索阅读辅助系统的设计选择。该项目将填补这一研究中的关键空白，并将调查提高DHH计算工作者的学习和阅读辅助需求，影响阅读辅助工具可用性的设计参数，针对特定读者群体和技术类型量身定制的文本简化的新培训数据和方法，自动和基于人的评估方法，以及这些工具对教学学习的影响。这些方法将包括对DHH计算工作者的访谈和调查研究、设计变化的原型和测试、平行简化语料库的创建、DHH个人对词汇和文本的可读性注释、领域适应的NLP研究和基于语法的文本简化神经机器翻译。并与DHH的学生和国家聋人技术研究所（NTID）计算机相关专业的应届毕业生一起观察原型在现实世界中的部署。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。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Discourse Level Factors for Sentence Deletion in Text Simplification

DOI：
10.1609/aaai.v34i05.6520
发表时间：
2019-11
期刊：
影响因子：
0
作者：
Yang Zhong;Chao Jiang;W. Xu;Junyi Jessy Li
通讯作者：
Yang Zhong;Chao Jiang;W. Xu;Junyi Jessy Li

arXivEdits: Understanding the Human Revision Process in Scientific Writing

arXivEdits：了解科学写作中的人工修改过程

DOI：
发表时间：
2022
期刊：
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
影响因子：
0
作者：
Jiang, Chao;Xu, Wei;Stevens, Samuel
通讯作者：
Stevens, Samuel

A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification