15,583 research outputs found

    Individual and Domain Adaptation in Sentence Planning for Dialogue

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    One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations

    I quit, therefore I am?: volunteer turnover and the politics of self-actualization

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    This study considers the thesis that volunteering is gaining a fundamentally new quality as a result of broader social and cultural transformations. Whereas existing research has focused on the changing nature of volunteering, this study deals with the decision to quit volunteering and examines whether it may be considered part of the "politics of self-actualization," that is, the more active and individualized monitoring of life. Former styles of volunteering and reasons for quitting were examined in a group of 99 ex-volunteers of the Red Cross in Flanders, Belgium, and volunteering habits were compared with a sample of 652 volunteers. Ex-volunteers did not systematically differ from the sample of volunteers with regard to their social background profile, volunteering behavior, and strength of organizational attachment. Furthermore, the decision to quit more likely reflected the routine nature of everyday practices than an autonomous and self-conscious life design

    Towards estimating computer users' mood from interaction behaviour with keyboard and mouse

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    The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study (n = 26) and a controlled study (n = 16) were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response (GSR) data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the results of the studies found significant average correlations between mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is possible from interaction behaviour with keyboard and mouse

    Data, Data Everywhere, and Still Too Hard to Link: Insights from User Interactions with Diabetes Apps

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    For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. How- ever, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions
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