We present new results from a real-user evaluation of a data-driven approach to learning user-adaptive referring expression generation (REG) policies for spoken dialogue systems. ...
Given the difficulty of setting up large-scale experiments with real users, the comparison of content-based image retrieval methods using relevance feedback usually relies on the ...
Michel Crucianu, Jean-Philippe Tarel, Marin Fereca...
Although some guidelines (e.g., based on architectural principles) have been proposed for designing Virtual Environments (VEs), several usability problems can be identified only b...
The ultimate goal when building dialogue systems is to satisfy the needs of real users, but quality assurance for dialogue strategies is a non-trivial problem. The applied evaluat...
We address two problems in the field of automatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and evaluating...
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to de...
Users’ critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system’s accuracy in recommendations that ...