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        <title>Incremental Graph-based Neural Dependency Parsing</title>
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        <namePart type="given">Xiaoqing</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Martha</namePart>
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            <namePart type="family">Hwa</namePart>
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    <abstract>Very recently, some studies on neural dependency parsers have shown advantage over the traditional ones on a wide variety of languages. However, for graph-based neural dependency parsing systems, they either count on the long-term memory and attention mechanism to implicitly capture the high-order features or give up the global exhaustive inference algorithms in order to harness the features over a rich history of parsing decisions. The former might miss out the important features for specific headword predictions without the help of the explicit structural information, and the latter may suffer from the error propagation as false early structural constraints are used to create features when making future predictions. We explore the feasibility of explicitly taking high-order features into account while remaining the main advantage of global inference and learning for graph-based parsing. The proposed parser first forms an initial parse tree by head-modifier predictions based on the first-order factorization. High-order features (such as grandparent, sibling, and uncle) then can be defined over the initial tree, and used to refine the parse tree in an iterative fashion. Experimental results showed that our model (called INDP) archived competitive performance to existing benchmark parsers on both English and Chinese datasets.</abstract>
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    <identifier type="doi">10.18653/v1/D17-1173</identifier>
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%0 Conference Proceedings
%T Incremental Graph-based Neural Dependency Parsing
%A Zheng, Xiaoqing
%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 zheng-2017-incremental
%X Very recently, some studies on neural dependency parsers have shown advantage over the traditional ones on a wide variety of languages. However, for graph-based neural dependency parsing systems, they either count on the long-term memory and attention mechanism to implicitly capture the high-order features or give up the global exhaustive inference algorithms in order to harness the features over a rich history of parsing decisions. The former might miss out the important features for specific headword predictions without the help of the explicit structural information, and the latter may suffer from the error propagation as false early structural constraints are used to create features when making future predictions. We explore the feasibility of explicitly taking high-order features into account while remaining the main advantage of global inference and learning for graph-based parsing. The proposed parser first forms an initial parse tree by head-modifier predictions based on the first-order factorization. High-order features (such as grandparent, sibling, and uncle) then can be defined over the initial tree, and used to refine the parse tree in an iterative fashion. Experimental results showed that our model (called INDP) archived competitive performance to existing benchmark parsers on both English and Chinese datasets.
%R 10.18653/v1/D17-1173
%U https://aclanthology.org/D17-1173
%U https://doi.org/10.18653/v1/D17-1173
%P 1655-1665

Markdown (Informal)

[Incremental Graph-based Neural Dependency Parsing](https://aclanthology.org/D17-1173) (Zheng, EMNLP 2017)

• Incremental Graph-based Neural Dependency Parsing (Zheng, EMNLP 2017)

ACL

• Xiaoqing Zheng. 2017. Incremental Graph-based Neural Dependency Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1655–1665, Copenhagen, Denmark. Association for Computational Linguistics.