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    <titleInfo>
        <title>In-Order Transition-based Constituent Parsing</title>
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    <name type="personal">
        <namePart type="given">Jiangming</namePart>
        <namePart type="family">Liu</namePart>
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    <abstract>Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction. To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on the WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.</abstract>
    <identifier type="citekey">liu-zhang-2017-order</identifier>
    <identifier type="doi">10.1162/tacl_a_00070</identifier>
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        <url>https://aclanthology.org/Q17-1029</url>
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        <date>2017</date>
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%0 Journal Article
%T In-Order Transition-based Constituent Parsing
%A Liu, Jiangming
%A Zhang, Yue
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F liu-zhang-2017-order
%X Both bottom-up and top-down strategies have been used for neural transition-based constituent parsing. The parsing strategies differ in terms of the order in which they recognize productions in the derivation tree, where bottom-up strategies and top-down strategies take post-order and pre-order traversal over trees, respectively. Bottom-up parsers benefit from rich features from readily built partial parses, but lack lookahead guidance in the parsing process; top-down parsers benefit from non-local guidance for local decisions, but rely on a strong encoder over the input to predict a constituent hierarchy before its construction. To mitigate both issues, we propose a novel parsing system based on in-order traversal over syntactic trees, designing a set of transition actions to find a compromise between bottom-up constituent information and top-down lookahead information. Based on stack-LSTM, our psycholinguistically motivated constituent parsing system achieves 91.8 F1 on the WSJ benchmark. Furthermore, the system achieves 93.6 F1 with supervised reranking and 94.2 F1 with semi-supervised reranking, which are the best results on the WSJ benchmark.
%R 10.1162/tacl_a_00070
%U https://aclanthology.org/Q17-1029
%U https://doi.org/10.1162/tacl_a_00070
%P 413-424

Markdown (Informal)

[In-Order Transition-based Constituent Parsing](https://aclanthology.org/Q17-1029) (Liu & Zhang, TACL 2017)

• In-Order Transition-based Constituent Parsing (Liu & Zhang, TACL 2017)

ACL

• Jiangming Liu and Yue Zhang. 2017. In-Order Transition-based Constituent Parsing. Transactions of the Association for Computational Linguistics, 5:413–424.