Our goal in this paper is to establish a means for a dialogue platform to be able to cope with open domains considering the possible interaction between the embodied agent and humans. To this end we present an algorithm capable of processing natural language utterances and validate them against knowledge structures of an intelligent agent's mind. Our algorithm leverages dialogue techniques in order to solve ambiguities and acquire knowledge about unknown entities.
On the other hand, Bohus and Horvitz @cite_6 proposed an open-world platform which attempts to allow a dialogue system to support multi-dynamic user interaction along with heavily situated context information acquired, mostly from vision features, to adapt the dialogue domain. We show that open domain dialogue adaptation can also rely on linguistic information rather than there approach, which focused mainly on visual information.
@cite_6
2128093162
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.