Training dependency parsers by jointly optimizing multiple objectives

  • Authors:
  • Keith Hall;Ryan McDonald;Jason Katz-Brown;Michael Ringgaard

  • Affiliations:
  • Google Research;Google Research;Google Research;Google Research

  • Venue:
  • EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2011

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Abstract

We present an online learning algorithm for training parsers which allows for the inclusion of multiple objective functions. The primary example is the extension of a standard supervised parsing objective function with additional loss-functions, either based on intrinsic parsing quality or task-specific extrinsic measures of quality. Our empirical results show how this approach performs for two dependency parsing algorithms (graph-based and transition-based parsing) and how it achieves increased performance on multiple target tasks including reordering for machine translation and parser adaptation.