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


PIs: Wei Xu, Ohio State University (Award Details)
Matt Huenerfauth, Rochester Institute of Technology (Award Details)

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).

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