Text generation: using discourse strategies and focus constraints to generate natural language text
Text generation: using discourse strategies and focus constraints to generate natural language text
UC -- A Progress Report
Correcting object-related misconceptions (natural language)
Correcting object-related misconceptions (natural language)
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
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If natural language question-answering (NLQA) systems are to be truly effective and useful, they must respond to queries cooperatively, recognizing and accommodating in their replies a questioner's goals, plans, and needs. This paper concerns the design of cooperative response generation (CRG) systems, NLQA systems that are able to produce integrated cooperative responses. We propose two characteristics of a computational model of cooperative response generation. First, we argue that CRG systems should be able to explicitly reason about and choose among the different response options available to them in a given situation. Second, we suggest that some choices of response content motivate others---that through a process called reflection, respondents detect the need to explain, justify, clarify or otherwise augment information they have already decided to convey.