Generation of referring expressions: Assessing the Incremental Algorithm (original) (raw)
Abstract A substantial amount of recent work in natural language generation has focused on the generation of ''one-shot''referring expressions whose only aim is to identify a target referent. Dale and Reiter's Incremental Algorithm (IA) is often thought to be the best algorithm for maximizing the similarity to referring expressions produced by people. We test this hypothesis by eliciting referring expressions from human subjects and computing the similarity between the expressions elicited and the ones generated by algorithms.
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