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        <title>Window-Based Neural Tagging for Shallow Discourse Argument Labeling</title>
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        <namePart type="given">René</namePart>
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    <name type="personal">
        <namePart type="given">Manfred</namePart>
        <namePart type="family">Stede</namePart>
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    <name type="personal">
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
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    <abstract>This paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing. Our method describes a decomposition of the overall labeling task into subtasks and a general distance-based aggregation procedure. For learning these subtasks, we train a recurrent neural network and gradually replace existing components of our baseline by our model. The model is trained and evaluated on the Penn Discourse Treebank 2 corpus. While it is not as good as knowledge-intense approaches, it clearly outperforms other models that are also trained without additional linguistic features.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1072</identifier>
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%0 Conference Proceedings
%T Window-Based Neural Tagging for Shallow Discourse Argument Labeling
%A Knaebel, René
%A Stede, Manfred
%A Stober, Sebastian
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F knaebel-etal-2019-window
%X This paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing. Our method describes a decomposition of the overall labeling task into subtasks and a general distance-based aggregation procedure. For learning these subtasks, we train a recurrent neural network and gradually replace existing components of our baseline by our model. The model is trained and evaluated on the Penn Discourse Treebank 2 corpus. While it is not as good as knowledge-intense approaches, it clearly outperforms other models that are also trained without additional linguistic features.
%R 10.18653/v1/K19-1072
%U https://aclanthology.org/K19-1072
%U https://doi.org/10.18653/v1/K19-1072
%P 768-777

Markdown (Informal)

[Window-Based Neural Tagging for Shallow Discourse Argument Labeling](https://aclanthology.org/K19-1072) (Knaebel et al., CoNLL 2019)

• Window-Based Neural Tagging for Shallow Discourse Argument Labeling (Knaebel et al., CoNLL 2019)

ACL

• René Knaebel, Manfred Stede, and Sebastian Stober. 2019. Window-Based Neural Tagging for Shallow Discourse Argument Labeling. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 768–777, Hong Kong, China. Association for Computational Linguistics.