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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Is it that difficult to find a good preference order for the incremental algorithm?

Emiel Krahmer1, Ruud Koolen, Mariët Theune

  • 1Tilburg Center for Cognition and Communication-TiCC, School of Humanities, Tilburg University, P.O. Box 90153, NL-5000 LE, Tilburg, The Netherlands. e.j.krahmer@uvt.nl

Cognitive Science
|June 8, 2012
PubMed
Summary
This summary is machine-generated.

Finding a suitable Preference Order for generating referring expressions is easier than previously thought. A learning curve experiment shows that a few human descriptions can help determine a good order, especially for the head of the order.

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Area of Science:

  • Natural Language Generation
  • Computational Linguistics
  • Cognitive Science

Background:

  • The Incremental Algorithm for referring expression generation relies heavily on a domain-specific Preference Order.
  • Previous work suggests determining an appropriate Preference Order is challenging due to a lack of evidence.

Purpose of the Study:

  • To investigate the difficulty of establishing a Preference Order for new domains.
  • To challenge the notion that determining a good Preference Order is inherently difficult.

Main Methods:

  • A learning curve experiment was conducted.
  • Human-produced descriptions were collected in a semantically transparent manner.

Main Results:

  • Finding a suitable Preference Order for a new domain is feasible with limited human data.
  • The head of a Preference Order is more critical and easier to determine than the tail.

Conclusions:

  • The reliance on pre-determined Preference Orders in referring expression generation may be less problematic than argued.
  • Accessible human descriptions facilitate the efficient creation of effective Preference Orders.