Heuristic reasoning about uncertainty: an artificial intelligence approach
Heuristic reasoning about uncertainty: an artificial intelligence approach
Artificial Intelligence
Modeling the user's plans and goals
Computational Linguistics - Special issue on user modeling
Recognizing and responding to plan-oriented misconceptions
Computational Linguistics - Special issue on user modeling
Reasoning on a highlighted user model to respond to misconceptions
Computational Linguistics - Special issue on user modeling
Pragmatics and natural language generation
Artificial Intelligence
Linguistic and pragmatic constraints on utterance interpretation
Linguistic and pragmatic constraints on utterance interpretation
Communicative acts for explanation generation
International Journal of Man-Machine Studies
A problem for RST: the need for multi-level discourse analysis
Computational Linguistics
Participating in explanatory dialogues: interpreting and responding to questions in context
Participating in explanatory dialogues: interpreting and responding to questions in context
A computational model for generating and interpreting indirect answers
A computational model for generating and interpreting indirect answers
Plan Recognition in Natural Language Dialogue
Plan Recognition in Natural Language Dialogue
An Evidential Model for Tracking Initiative in Collaborative Dialogue Interactions
User Modeling and User-Adapted Interaction
An Analysis of Initiative Selection in CollaborativeTask-Oriented Discourse
User Modeling and User-Adapted Interaction
IDP - An Interactive Discourse Planner
EWNLG '93 Selected papers from the Fourth European Workshop on Trends in Natural Language Generation, An Artificial Intelligence Perspective
A computational theory of goal-directed style in syntax
Computational Linguistics
Planning text for advisory dialogues: capturing intentional and rhetorical information
Computational Linguistics
A plan-based analysis of indirect speech acts
Computational Linguistics
Computational Linguistics
A pragmatics-based approach to ellipsis resolution
Computational Linguistics
A model of plan inference that distinguishes between the beliefs of actors and observers
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
A model for generating better explanations
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
A tripartite plan-based model of dialogue
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Modeling negotiation subdialogues
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
Cues and control in expert-client dialogues
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Planning coherent multisentential text
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Using linguistic, world, and contextual knowledge in a plan recognition model of dialogue
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
An Approach to Mixed Initiative Spoken Information Retrieval Dialogue
User Modeling and User-Adapted Interaction
Natural Language Processing and User Modeling: Synergies and Limitations
User Modeling and User-Adapted Interaction
Empirical Evaluation of User Models and User-Adapted Systems
User Modeling and User-Adapted Interaction
An Analysis of Conditional Responses in Dialogue
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
Conditional responses in information-seeking dialogues
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
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Initiative in dialogue can be regarded as the speaker taking the opportunity to contribute more information than was his obligation in a particular discourse turn. This paper describes the use of stimulus conditions as a computational mechanism for taking the initiative to provide unrequested information in responses to Yes–No questions, as part of a system for generating answers to Yes–No questions. Stimulus conditions represent types of discourse contexts in which a speaker is motivated to add unrequested information to his answer. Stimulus conditions may be triggered not only by the discourse context at the time when the question was asked, but also by the anticipated context resulting from providing part of the response. We define a set of stimulus conditions based upon previous linguistic studies and a corpus analysis, and describe how evaluation of these stimulus conditions makes use of information from a User Model. Also, we show how the stimulus conditions are used by the generation component of the system. An evaluation was conducted of the implemented system. The results indicate that the responses generated by our system containing extra information provided on the basis of this initiative mechanism are viewed more favorably by users than responses without the extra information.