The need for accurate alignment in natural language system evaluation

  • Authors:
  • Andrew Kehler;John Bear;Douglas Appelt

  • Affiliations:
  • University of California, San Diego, La Jolla, CA;Artificial Intelligence Center, Menlo Park, CA;Artificial Intelligence Center, Menlo Park, CA

  • Venue:
  • Computational Linguistics
  • Year:
  • 2001

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Abstract

As evaluations of computational linguistics technology progress toward higher-level interpretation tasks, the problem of determining alignments between system responses and answer key entries may become less straightforward. We present an extensive analysis of the alignment procedure used in the MUC-6 evaluation of information extraction technology, which reveals effects that interfere with the stated goals of the evaluation. These effects are shown to be pervasive enough that they have the potential to adversely impact the technology development process. These results argue strongly/ or the use of accurate alignment criteria in natural language evaluations, and/ or maintaining the independence of alignment criteria and mechanisms used to calculate scores.