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        <title>Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set</title>
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    <abstract>We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case O(n³) exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also present O(n³) global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the “second-best-in-class” result on the English Penn Treebank.</abstract>
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%0 Conference Proceedings
%T Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
%A Shi, Tianze
%A Huang, Liang
%A Lee, Lillian
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F shi-etal-2017-fast
%X We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features. We plug our minimal feature set into the dynamic-programming framework of Huang and Sagae (2010) and Kuhlmann et al. (2011) to produce the first implementation of worst-case O(n³) exact decoders for arc-hybrid and arc-eager transition systems. With our minimal features, we also present O(n³) global training methods. Finally, using ensembles including our new parsers, we achieve the best unlabeled attachment score reported (to our knowledge) on the Chinese Treebank and the “second-best-in-class” result on the English Penn Treebank.
%R 10.18653/v1/D17-1002
%U https://aclanthology.org/D17-1002
%U https://doi.org/10.18653/v1/D17-1002
%P 12-23

Markdown (Informal)

[Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set](https://aclanthology.org/D17-1002) (Shi et al., EMNLP 2017)

• Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set (Shi et al., EMNLP 2017)

ACL

• Tianze Shi, Liang Huang, and Lillian Lee. 2017. Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 12–23, Copenhagen, Denmark. Association for Computational Linguistics.