Turbo parsers: dependency parsing by approximate variational inference

  • Authors:
  • André F. T. Martins;Noah A. Smith;Eric P. Xing;Pedro M. Q. Aguiar;Mário A. T. Figueiredo

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA and Instituto Superior Técnico, Lisboa, Portugal;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Instituto Superior Técnico, Lisboa, Portugal;Instituto Superior Técnico, Lisboa, Portugal

  • Venue:
  • EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2010

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Abstract

We present a unified view of two state-of-the-art non-projective dependency parsers, both approximate: the loopy belief propagation parser of Smith and Eisner (2008) and the relaxed linear program of Martins et al. (2009). By representing the model assumptions with a factor graph, we shed light on the optimization problems tackled in each method. We also propose a new aggressive online algorithm to learn the model parameters, which makes use of the underlying variational representation. The algorithm does not require a learning rate parameter and provides a single framework for a wide family of convex loss functions, including CRFs and structured SVMs. Experiments show state-of-the-art performance for 14 languages.