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    <titleInfo>
        <title>Learning Structured Text Representations</title>
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    <name type="personal">
        <namePart type="given">Yang</namePart>
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    <abstract>In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias (Cheng et al., 2016; Kim et al., 2017), we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluations across different tasks and datasets show that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.</abstract>
    <identifier type="citekey">liu-lapata-2018-learning</identifier>
    <identifier type="doi">10.1162/tacl_a_00005</identifier>
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        <date>2018</date>
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            <start>63</start>
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%0 Journal Article
%T Learning Structured Text Representations
%A Liu, Yang
%A Lapata, Mirella
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F liu-lapata-2018-learning
%X In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias (Cheng et al., 2016; Kim et al., 2017), we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluations across different tasks and datasets show that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.
%R 10.1162/tacl_a_00005
%U https://aclanthology.org/Q18-1005
%U https://doi.org/10.1162/tacl_a_00005
%P 63-75

Markdown (Informal)

[Learning Structured Text Representations](https://aclanthology.org/Q18-1005) (Liu & Lapata, TACL 2018)

• Learning Structured Text Representations (Liu & Lapata, TACL 2018)

ACL

• Yang Liu and Mirella Lapata. 2018. Learning Structured Text Representations. Transactions of the Association for Computational Linguistics, 6:63–75.