Refining instructional text generation after evaluation.
In this paper, we describe how user-adapted explanations about drug prescriptions can be generated from already existing data sources. We start by illustrating the two-step approach employed in the first version of the natural language generator and the limitations of generated texts, that we discovered through analytical and empirical evaluations. We claim that, although style refinement would be needed in these texts, particular care should be devoted to implementing some of the persuasion techniques that doctors employ in their explanations. This would require either thoroughly revising the text planning techniques employed or converting to a multistep generation architecture. We justify why we selected this second alternative and propose some heuristics to repair problems found in the first version of the generator. Some final considerations about the advantages of this approach and the possibility of generalizing it to other domains conclude the paper.
de Rosis
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Dipartimento di Informatica, Università di Bari, Italy. derosis@gauss.uniba.it
Grasso
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Berry
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D000319:Adrenergic beta-Antagonists / Q000627:therapeutic use; D000787:Angina Pectoris / Q000188:drug therapy; D001185:Artificial Intelligence; D001241:Aspirin / Q000627:therapeutic use; D001262:Atenolol / Q000627:therapeutic use; D003658:Decision Making, Computer-Assisted; D011307:Drug Prescriptions; D005069:Evaluation Studies as Topic; D006801:Humans; D008297:Male; D008875:Middle Aged
D000319:Adrenergic beta-Antagonists; D001262:Atenolol; D001241:Aspirin