@article{hayashi-etal-2013-efficient,
    title = "Efficient Stacked Dependency Parsing by Forest Reranking",
    author = "Hayashi, Katsuhiko  and
      Kondo, Shuhei  and
      Matsumoto, Yuji",
    editor = "Lin, Dekang  and
      Collins, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "1",
    year = "2013",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/Q13-1012/",
    doi = "10.1162/tacl_a_00216",
    pages = "139--150",
    abstract = "This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. To improve efficiency and accuracy, this paper also proposes a novel shift-reduce parser that eliminates the spurious ambiguity of arc-standard transition systems. Testing on the English Penn Treebank data, forest reranking gave a state-of-the-art unlabeled dependency accuracy of 93.12."
}