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        <title>A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing</title>
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    <name type="personal">
        <namePart type="given">Zhihua</namePart>
        <namePart type="family">Liao</namePart>
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        <namePart type="family">Xie</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>In this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentence-to-lambda-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the lambda-logical expression structure into a form suitable for the mechanics of statistical machine translation and useful for modeling. We show that our model is able to yield new state-of-the-art results on both standard datasets with simple features.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_059</identifier>
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        <url>https://aclanthology.org/R17-1059/</url>
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%0 Conference Proceedings
%T A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing
%A Liao, Zhihua
%A Xie, Yan
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F liao-xie-2017-statistical
%X In this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentence-to-lambda-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the lambda-logical expression structure into a form suitable for the mechanics of statistical machine translation and useful for modeling. We show that our model is able to yield new state-of-the-art results on both standard datasets with simple features.
%R 10.26615/978-954-452-049-6_059
%U https://aclanthology.org/R17-1059/
%U https://doi.org/10.26615/978-954-452-049-6_059
%P 446-451

Markdown (Informal)

[A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing](https://aclanthology.org/R17-1059/) (Liao & Xie, RANLP 2017)

• A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing (Liao & Xie, RANLP 2017)

ACL

• Zhihua Liao and Yan Xie. 2017. A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 446–451, Varna, Bulgaria. INCOMA Ltd..