Understanding user state and preferences for robust spoken dialog systems and location-aware assistive technology

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and, (S.M. in Technology and Policy)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 119-125).This research focuses on improving the performance of spoken dialog systems (SDS) in the domain of assistive technology for people with disabilities. Automatic speech recognition (ASR) has compelling potential applications as a means of enabling people with physical disabilities to enjoy greater levels of independence and participation. This thesis describes the development and evaluation of a spoken dialog system modeled as a partially observable Markov decision process (SDS-POMDP). The SDSPOMDP can understand commands related to making phone calls and providing information about weather, activities, and menus in a specialized-care residence setting. Labeled utterance data was used to train observation and utterance confidence models. With a user simulator, the SDS-POMDP reward function parameters were optimized, and the SDS-POMDP is shown to out-perform simpler threshold-based dialog strategies. These simulations were validated in experiments with human participants, with the SDS-POMDP resulting in more successful dialogs and faster dialog completion times, particularly for speakers with high word-error rates. This thesis also explores the social and ethical implications of deploying location based assistive technology in specialized-care settings. These technologies could have substantial potential benefit to residents and caregivers in such environments, but they may also raise issues related to user safety, independence, autonomy, or privacy. As one example, location-aware mobile devices are potentially useful to increase the safety of individuals in a specialized-care setting who may be at risk of unknowingly wandering, but they raise important questions about privacy and informed consent. This thesis provides a survey of U.S. legislation related to the participation of individuals who have questionable capacity to provide informed consent in research studies. Overall, it seeks to precisely describe and define the key issues that are arise as a result of new, unforeseen technologies that may have both benefits and costs to the elderly and people with disabilities.by William Li.S.M.in Technology and PolicyS.M

Language technologies for understanding law, politics, and public policy

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 205-209).This thesis focuses on the development of machine learning and natural language processing methods and their application to large, text-based open government datasets. We focus on models that uncover patterns and insights by inferring the origins of legal and political texts, with a particular emphasis on identifying text reuse and text similarity in these document collections. First, we present an authorship attribution model on unsigned U.S. Supreme Court opinions, offering insights into the authorship of important cases and the dynamics of Supreme Court decision-making. Second, we apply software engineering metrics to analyze the complexity of the United States Code of Laws, thereby illustrating the structure and evolution of the U.S. Code over the past century. Third, we trace policy trajectories of legislative bills in the United States Congress, enabling us to visualize the contents of four key bills during the Financial Crisis. These applications on diverse open government datasets reveal that text reuse occurs widely in legal and political texts: similar ideas often repeat in the same corpus, different historical versions of documents are usually quite similar, or legitimate reasons for copying or borrowing text may exist. Motivated by this observation, we present a novel statistical text model, Probabilistic Text Reuse (PTR), for finding repeated passages of text in large document collections. We illustrate the utility of PTR by finding template ideas, less-common voices, and insights into document structure in a large collection of public comments on regulations proposed by the U.S. Federal Communications Commission (FCC) on net neutrality. These techniques aim to help citizens better understand political processes and help governments better understand political speech.by William P. Li.Ph. D