Evaluation in natural language generation: lessons from referring expression generation

Jette Viethen, Robert Dale

Research output: Contribution to journalArticlepeer-review


As one of the most well-defined subtasks in Natural Language Generation (NLG), the generation of referring expressions looks like a strong candidate for piloting shared evaluation tasks. Different to other areas of Natural Language Processing, it is still unclear what benefit the introduction of such tasks might have for the field of NLG. Based on an earlier evaluation of a number of well-established algorithms for the generation of referring expressions, this paper explores several problems that arise in designing evaluation for this task, and identifies general considerations that need to be met in evaluating Natural Language Generation subtasks.
Original languageEnglish
Pages (from-to)141-160
Number of pages20
JournalTraitement automatique des langues : TAL
Issue number1
Publication statusPublished - 2007


  • Referring Expression Generation
  • Natural Language Generation
  • Evaluation

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