Optimizing for Measure of Performance in Max-Margin Parsing

Alexander Bauer, Shinichi Nakajima, Nico Gornitz, Klaus Robert Muller

Research output: Contribution to journalArticlepeer-review

Abstract

Many learning tasks in the field of natural language processing including sequence tagging, sequence segmentation, and syntactic parsing have been successfully approached by means of structured prediction methods. An appealing property of the corresponding training algorithms is their ability to integrate the loss function of interest into the optimization process improving the final results according to the chosen measure of performance. Here, we focus on the task of constituency parsing and show how to optimize the model for the $F_{1}$-score in the max-margin framework of a structural support vector machine (SVM). For reasons of computational efficiency, it is a common approach to binarize the corresponding grammar before training. Unfortunately, this introduces a bias during the training procedure as the corresponding loss function is evaluated on the binary representation, while the resulting performance is measured on the original unbinarized trees. Here, we address this problem by extending the inference procedure presented by Bauer et al. Specifically, we propose an algorithmic modification that allows evaluating the loss on the unbinarized trees. The new approach properly models the loss function of interest resulting in better prediction accuracy and still benefits from the computational efficiency due to binarized representation. The presented idea can be easily transferred to other structured loss functions.

Original languageEnglish
Article number8825553
Pages (from-to)2680-2684
Number of pages5
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number7
DOIs
Publication statusPublished - 2020 Jul

Keywords

  • Dynamic programming
  • graphical models
  • high-order potentials
  • inference
  • margin scaling
  • slack scaling
  • structural support vector machines (SVMs)
  • structured output

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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