A surprisingly large number of Americans read below their grade level, either because of limited education or because their native language is not English. Low reading levels impact a child?s progress in school and an adult?s job opportunities as well as limiting information access. This project aims to improve access by developing new language processing technology for selecting and transforming text to obtain material at lower reading levels, extending current paraphrasing work that focuses on summarization as compression to include explanatory expansions. In addition, the goal is to develop adaptive models that can be tuned to a specific domain and an individual's needs. The approach involves analyzing corpora of comparable text collected from the web, developing models of paraphrasing aimed at generating simplified English, developing a discourse-sensitive clause selection method for expanding or omitting details, and exploring representations of language that facilitate domain and user adaptation. The language processing contributions of this work include development of text resources to support language technology in education applications, new representations of reading difficulty, and advances in automatic methods of paraphrasing. The broader impact of this project includes making information more accessible to people with limited English reading proficiency. In addition, students working on the project will have the opportunity to interact with teachers from a local school so as to better understand the impact of their work and guide their approach, and their work will be showcased in University of Washington diversity-oriented outreach programs.
