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

Probabilistic Modeling in Dynamic Information Retrieval

Dynamic modeling is used to design systems that are adaptive to their changing environment and is currently poorly understood in information retrieval systems. Common elements in the information retrieval methodology, such as documents, relevance, users and tasks, are dynamic entities that may evolve over the course of several interactions, which is increasingly captured in search log datasets. Conventional frameworks and models in information retrieval treat these elements as static, or only consider local interactivity, without consideration for the optimisation of all potential interactions. Further to this, advances in information retrieval interface, contextual personalization and ad display demand models that can intelligently react to users over time. This thesis proposes a new area of information retrieval research called Dynamic Information Retrieval. The term dynamics is defined and what it means within the context of information retrieval. Three examples of current areas of research in information retrieval which can be described as dynamic are covered: multi-page search, online learning to rank and session search. A probabilistic model for dynamic information retrieval is introduced and analysed, and applied in practical algorithms throughout. This framework is based on the partially observable Markov decision process model, and solved using dynamic programming and the Bellman equation. Comparisons are made against well-established techniques that show improvements in ranking quality and in particular, document diversification. The limitations of this approach are explored and appropriate approximation techniques are investigated, resulting in the development of an efficient multi-armed bandit based ranking algorithm. Finally, the extraction of dynamic behaviour from search logs is also demonstrated as an application, showing that dynamic information retrieval modeling is an effective and versatile tool in state of the art information retrieval research

Exploiting user signals and stochastic models to improve information retrieval systems and evaluation

The leitmotiv throughout this thesis is represented by IR evaluation. We discuss different issues related to effectiveness measures and novel solutions that we propose to address these challenges. We start by providing a formal definition of utility-oriented measurement of retrieval effectiveness, based on the representational theory of measurement. The proposed theoretical framework contributes to a better understanding of the problem complexities, separating those due to the inherent problems in comparing systems, from those due to the expected numerical properties of measures. We then propose AWARE, a probabilistic framework for dealing with the noise and inconsistencies introduced when relevance labels are gathered with multiple crowd assessors. By modeling relevance judgements and crowd assessors as sources of uncertainty, we directly combine the performance measures computed on the ground-truth generated by each crowd assessor, instead of adopting a classification technique to merge the labels at pool level. Finally, we investigate evaluation measures able to account for user signals. We propose a new user model based on Markov chains, that allows the user to scan the result list with many degrees of freedom. We exploit this Markovian model in order to inject user models into precision, defining a new family of evaluation measures, and we embed this model as objective function of an LtR algorithm to improve system performances