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        <title>Representation Learning and Dynamic Programming for Arc-Hybrid Parsing</title>
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        <namePart type="given">Joseph</namePart>
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        <namePart type="given">Antoine</namePart>
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    <name type="personal">
        <namePart type="given">Mathieu</namePart>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
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    <abstract>We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1023</identifier>
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%0 Conference Proceedings
%T Representation Learning and Dynamic Programming for Arc-Hybrid Parsing
%A Le Roux, Joseph
%A Rozenknop, Antoine
%A Lacroix, Mathieu
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F le-roux-etal-2019-representation
%X We present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From this representation we are able to derive an efficient parsing algorithm and design a neural network that learns vertex representations and arc scores. Experimentally, although we only train via local classifiers, our approach improves over previous arc-hybrid systems and reach state-of-the-art parsing accuracy.
%R 10.18653/v1/K19-1023
%U https://aclanthology.org/K19-1023
%U https://doi.org/10.18653/v1/K19-1023
%P 238-248

Markdown (Informal)

[Representation Learning and Dynamic Programming for Arc-Hybrid Parsing](https://aclanthology.org/K19-1023) (Le Roux et al., CoNLL 2019)

• Representation Learning and Dynamic Programming for Arc-Hybrid Parsing (Le Roux et al., CoNLL 2019)

ACL

• Joseph Le Roux, Antoine Rozenknop, and Mathieu Lacroix. 2019. Representation Learning and Dynamic Programming for Arc-Hybrid Parsing. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 238–248, Hong Kong, China. Association for Computational Linguistics.