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        <title>Fast and Accurate Non-Projective Dependency Tree Linearization</title>
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    <name type="personal">
        <namePart type="given">Xiang</namePart>
        <namePart type="family">Yu</namePart>
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        <namePart type="given">Thang</namePart>
        <namePart type="family">Vu</namePart>
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    <name type="personal">
        <namePart type="given">Jonas</namePart>
        <namePart type="family">Kuhn</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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            <namePart type="given">Joyce</namePart>
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        <name type="personal">
            <namePart type="given">Natalie</namePart>
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            <namePart type="given">Joel</namePart>
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    <abstract>We propose a graph-based method to tackle the dependency tree linearization task. We formulate the task as a Traveling Salesman Problem (TSP), and use a biaffine attention model to calculate the edge costs. We facilitate the decoding by solving the TSP for each subtree and combining the solution into a projective tree. We then design a transition system as post-processing, inspired by non-projective transition-based parsing, to obtain non-projective sentences. Our proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.134</identifier>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T Fast and Accurate Non-Projective Dependency Tree Linearization
%A Yu, Xiang
%A Tannert, Simon
%A Vu, Ngoc Thang
%A Kuhn, Jonas
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F yu-etal-2020-fast
%X We propose a graph-based method to tackle the dependency tree linearization task. We formulate the task as a Traveling Salesman Problem (TSP), and use a biaffine attention model to calculate the edge costs. We facilitate the decoding by solving the TSP for each subtree and combining the solution into a projective tree. We then design a transition system as post-processing, inspired by non-projective transition-based parsing, to obtain non-projective sentences. Our proposed method outperforms the state-of-the-art linearizer while being 10 times faster in training and decoding.
%R 10.18653/v1/2020.acl-main.134
%U https://aclanthology.org/2020.acl-main.134
%U https://doi.org/10.18653/v1/2020.acl-main.134
%P 1451-1462

Markdown (Informal)

[Fast and Accurate Non-Projective Dependency Tree Linearization](https://aclanthology.org/2020.acl-main.134) (Yu et al., ACL 2020)

• Fast and Accurate Non-Projective Dependency Tree Linearization (Yu et al., ACL 2020)

ACL

• Xiang Yu, Simon Tannert, Ngoc Thang Vu, and Jonas Kuhn. 2020. Fast and Accurate Non-Projective Dependency Tree Linearization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1451–1462, Online. Association for Computational Linguistics.