Discourse strategies for generating natural-language text
Artificial Intelligence
Attention, intentions, and the structure of discourse
Computational Linguistics
Tailoring object descriptions to a user's level of expertise
Computational Linguistics - Special issue on user modeling
Planning interactive explanations
International Journal of Man-Machine Studies
Automated discourse generation using discourse structure relations
Artificial Intelligence - Special volume on natural language processing
A problem for RST: the need for multi-level discourse analysis
Computational Linguistics
Shared workspaces: how do they work and when are they useful?
International Journal of Man-Machine Studies
Informational redundancy and resource bounds in dialogue
Informational redundancy and resource bounds in dialogue
Planning text for advisory dialogues: capturing intentional and rhetorical information
Computational Linguistics
Planning text for advisory dialogues
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Mixed initiative in dialogue: an investigation into discourse segmentation
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Planning coherent multisentential text
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
Discourse and deliberation: testing a collaborative strategy
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Taking the initiative in natural language data base interactions: justifying why
COLING '82 Proceedings of the 9th conference on Computational linguistics - Volume 1
Redundancy in collaborative dialogue
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Deciding to remind during collaborative problem solving: empirical evidence for agent strategies
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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A discourse planner for,(task-oriented) dialogue must be able to make choices about whether relevant, but optional information (for example, the "satellites" in an RST-based planner) should be communicated. We claim that effective text planners must explicitly model aspects of the Hearer's cognitive state, such as what the hearer is attending to and what inferences the hearer can draw, in order to make these choices. We argue that a mere representation of the Hearer's knowledge is inadequate. We support this claim by (1) an analysis of naturally occurring dialogue, and (2) by simulating the generation of discourses in a situation in which we can vary the cognitive parameters of the hearer. Our results show that modeling cognitive state can lead to more effective discourses (measured with respect to a simple task).