Individual and Domain Adaptation in Sentence Planning for Dialogue

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

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Generating Effective Instructions: Knowing When to Stop

One aspect of Natural Language generation is describing entities so that they are distinguished from all other entities. Entities include objects, events, actions, and states. Much attention has been paid to objects and the generation of their referring expressions (descriptions meant to pick out or refer to an entity). However, a growing area of research is the automated generation of instruction manuals and an important part of generating instructions is distinguishing the actions that are to be carried out from other possible actions. One distinguishing feature is an action\u27s termination, or when the performance of the action is to stop. My dissertation work focuses on generating action descriptions from action information using the SPUD generation algorithm developed here at Penn by Matthew Stone. In my work, I concentrate on the generation of expressions of termination information as part of action descriptions. The problems I address include how termination information is represented in action information and expressed in Natural Language, how to determine when an action description allows the reader to understand how to perform the action correctly, and how to generate the appropriate description of action information

Deliberative Democracy and Complex Diversity. From Discourse Ethics to the Theory of Argumentation.

362 p.Can democracy accommodate contemporary diverse and complex societies? Is deliberation an appropiate means for these ends? Even in the face of violent conflict? What is the role of citizens? The central objetive of this thesis is to critically analyse the relationsship between complex diversity (Tully 2008, Kraus 2012) and deliberatibe democracy /Habermas 1996) from a systemic perspective (Masnbrige and Parkinson 2012). Thinking identity as complex diversity detaches identity from dichotomous categorisations either as public of private, civic or ethnic and, moral or political