Getting computers to talk like you and me
Getting computers to talk like you and me
Attention, intentions, and the structure of discourse
Computational Linguistics
Discovery procedures for sublanguage selectional patterns: initial experiments
Computational Linguistics
Plan recognition and discourse analysis: an integrated approach for understanding dialogues
Plan recognition and discourse analysis: an integrated approach for understanding dialogues
Wizard of Oz studies: why and how
IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
Optimization criteria for checkpoint placement
Communications of the ACM
Plan Recognition in Natural Language Dialogue
Plan Recognition in Natural Language Dialogue
A task independent oral dialogue model
EACL '91 Proceedings of the fifth conference on European chapter of the Association for Computational Linguistics
A dialogue manager using initiative-response units and distributed control
EACL '91 Proceedings of the fifth conference on European chapter of the Association for Computational Linguistics
A syntactic approach to discourse semantics
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
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This paper describes a method for the development of dialogue managers for natural language interfaces. A dialogue manager is presented designed on the basis of both a theoretical investigation of models for dialogue management and an analysis of empirical material. It is argued that for natural language interfaces many of the human interaction phenomena accounted for in, for instance, plan-based models of dialogue do not occur. Instead, for many applications, dialogue in natural-language interfaces can be managed from information on the functional role of an utterance as conveyed in the linguistic structure. This is modelled in a dialogue grammar which controls the interaction. Focus structure is handled using dialogue objects recorded in a dialogue tree which can be accessed through, it scoreboard by the various modules for interpretation, generation and background system access. A sublanguage approach is proposed. For each new application the Dialogue Manager is customized to meet the needs of the application. This requires empirical data which are collected through Wizard of Oz simulations. The corpus is used when updating the different knowledge sources involved in the natural language interface. In this paper the customization of the Dialogue Manager for database information retrieval applications is also described.