Salience as a Simplifying Metaphor for Natural Language Generation

David D. McDonald, E. Jeffery Conklin

We have developed a simple yet effective technique for planning the generation of natural language texts that describe photographs of natural scenes as processed by the UMass VISIONS system. The texts follow the ordering on the scene’s objects that is imposed by their visual salience -- an ordering which we believe is naturally computed as a by-product of visual processing, and thus is available -- for free -- as the basis for generating simple but effective texts without requiring the complex planning machinery often applied in generation. We suggest that it should be possible to find structural analogs to visual salience in other domains and to build comparably simple generation schemes based on them. We look briefly at how one such analogy might be drawn for the task of tutoring novice PASCAL programmers.


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