Vine Pruning for Efficient Multi-Pass Dependency Parsing

@inproceedings{Rush2012VinePF,
  title={Vine Pruning for Efficient Multi-Pass Dependency Parsing},
  author={Alexander M. Rush and Slav Petrov},
  booktitle={HLT-NAACL},
  year={2012}
}
Coarse-to-fine inference has been shown to be a robust approximate method for improving the efficiency of structured prediction models while preserving their accuracy. We propose a multi-pass coarse-to-fine architecture for dependency parsing using linear-time vine pruning and structured prediction cascades. Our first-, second-, and third-order models achieve accuracies comparable to those of their unpruned counterparts, while exploring only a fraction of the search space. We observe speed-ups… CONTINUE READING
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