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        <title>A Generative Parser with a Discriminative Recognition Algorithm</title>
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    <name type="personal">
        <namePart type="given">Jianpeng</namePart>
        <namePart type="family">Cheng</namePart>
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            <title>Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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        <name type="personal">
            <namePart type="given">Min-Yen</namePart>
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    <abstract>Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for parsing and language modeling which marries a generative model with a discriminative recognition model in an encoder-decoder setting. We provide interpretations of the framework based on expectation maximization and variational inference, and show that it enables parsing and language modeling within a single implementation. On the English Penn Treen-bank, our framework obtains competitive performance on constituency parsing while matching the state-of-the-art single-model language modeling score.</abstract>
    <identifier type="citekey">cheng-etal-2017-generative</identifier>
    <identifier type="doi">10.18653/v1/P17-2019</identifier>
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        <url>https://aclanthology.org/P17-2019</url>
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%0 Conference Proceedings
%T A Generative Parser with a Discriminative Recognition Algorithm
%A Cheng, Jianpeng
%A Lopez, Adam
%A Lapata, Mirella
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F cheng-etal-2017-generative
%X Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for parsing and language modeling which marries a generative model with a discriminative recognition model in an encoder-decoder setting. We provide interpretations of the framework based on expectation maximization and variational inference, and show that it enables parsing and language modeling within a single implementation. On the English Penn Treen-bank, our framework obtains competitive performance on constituency parsing while matching the state-of-the-art single-model language modeling score.
%R 10.18653/v1/P17-2019
%U https://aclanthology.org/P17-2019
%U https://doi.org/10.18653/v1/P17-2019
%P 118-124

Markdown (Informal)

[A Generative Parser with a Discriminative Recognition Algorithm](https://aclanthology.org/P17-2019) (Cheng et al., ACL 2017)

• A Generative Parser with a Discriminative Recognition Algorithm (Cheng et al., ACL 2017)

ACL

• Jianpeng Cheng, Adam Lopez, and Mirella Lapata. 2017. A Generative Parser with a Discriminative Recognition Algorithm. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 118–124, Vancouver, Canada. Association for Computational Linguistics.