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        <title>A Unified Linear-Time Framework for Sentence-Level Discourse Parsing</title>
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    <name type="personal">
        <namePart type="given">Xiang</namePart>
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        <namePart type="given">Shafiq</namePart>
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    <name type="personal">
        <namePart type="given">Prathyusha</namePart>
        <namePart type="family">Jwalapuram</namePart>
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    <name type="personal">
        <namePart type="given">M</namePart>
        <namePart type="given">Saiful</namePart>
        <namePart type="family">Bari</namePart>
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    <abstract>We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse parser that constructs a discourse tree in a top-down fashion. Both the segmenter and the parser are based on Pointer Networks and operate in linear time. Our segmenter yields an F1 score of 95.4%, and our parser achieves an F1 score of 81.7% on the aggregated labeled (relation) metric, surpassing previous approaches by a good margin and approaching human agreement on both tasks (98.3 and 83.0 F1).</abstract>
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%0 Conference Proceedings
%T A Unified Linear-Time Framework for Sentence-Level Discourse Parsing
%A Lin, Xiang
%A Joty, Shafiq
%A Jwalapuram, Prathyusha
%A Bari, M. Saiful
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F lin-etal-2019-unified
%X We propose an efficient neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory (RST). Our framework comprises a discourse segmenter to identify the elementary discourse units (EDU) in a text, and a discourse parser that constructs a discourse tree in a top-down fashion. Both the segmenter and the parser are based on Pointer Networks and operate in linear time. Our segmenter yields an F1 score of 95.4%, and our parser achieves an F1 score of 81.7% on the aggregated labeled (relation) metric, surpassing previous approaches by a good margin and approaching human agreement on both tasks (98.3 and 83.0 F1).
%R 10.18653/v1/P19-1410
%U https://aclanthology.org/P19-1410
%U https://doi.org/10.18653/v1/P19-1410
%P 4190-4200

Markdown (Informal)

[A Unified Linear-Time Framework for Sentence-Level Discourse Parsing](https://aclanthology.org/P19-1410) (Lin et al., ACL 2019)

• A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (Lin et al., ACL 2019)

ACL

• Xiang Lin, Shafiq Joty, Prathyusha Jwalapuram, and M Saiful Bari. 2019. A Unified Linear-Time Framework for Sentence-Level Discourse Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4190–4200, Florence, Italy. Association for Computational Linguistics.