Getting the message across in RST-based text generation
Current research in natural language generation
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Evaluating a trainable sentence planner for a spoken dialogue system
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MATCH: an architecture for multimodal dialogue systems
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Optimising information presentation for spoken dialogue systems
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Natural language generation as planning under uncertainty for spoken dialogue systems
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INLG '12 Proceedings of the Seventh International Natural Language Generation Conference
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A challenging problem for spoken dialog systems is the design of utterance generation modules that are fast, flexible and general, yet produce high quality output in particular domains. A promising approach is trainable generation, which uses general-purpose linguistic knowledge automatically adapted to the application domain. This paper presents a trainable sentence planner for the MATCH dialog system. We show that trainable sentence planning can produce output comparable to that of MATCH's template-based generator even for quite complex information presentations.