Towards a Computational Account of Knowledge, Action and Inference in Instructions

I consider abstract instructions, which provide indirect descriptions of actions in cases when a speaker has key information that a hearer can use to identify the right action to perform, but the speaker alone cannot identify that action. Principled generation of abstract instructions requires a system to assess whether an instruction provides sufficient information for the user to draw appropriate inferences about action from it. I sketch a framework for specifying, computing, and accessing those assessments in natural language generation.

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