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        <title>Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition</title>
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    <name type="personal">
        <namePart type="given">Huibin</namePart>
        <namePart type="family">Ruan</namePart>
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        <namePart type="given">Yu</namePart>
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        <namePart type="given">Guodong</namePart>
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            <namePart type="given">Chengqing</namePart>
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    <abstract>We tackle implicit discourse relation recognition. Both self-attention and interactive-attention mechanisms have been applied for attention-aware representation learning, which improves the current discourse analysis models. To take advantages of the two attention mechanisms simultaneously, we develop a propagative attention learning model using a cross-coupled two-channel network. We experiment on Penn Discourse Treebank. The test results demonstrate that our model yields substantial improvements over the baselines (BiLSTM and BERT).</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.282</identifier>
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%0 Conference Proceedings
%T Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition
%A Ruan, Huibin
%A Hong, Yu
%A Xu, Yang
%A Huang, Zhen
%A Zhou, Guodong
%A Zhang, Min
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F ruan-etal-2020-interactively
%X We tackle implicit discourse relation recognition. Both self-attention and interactive-attention mechanisms have been applied for attention-aware representation learning, which improves the current discourse analysis models. To take advantages of the two attention mechanisms simultaneously, we develop a propagative attention learning model using a cross-coupled two-channel network. We experiment on Penn Discourse Treebank. The test results demonstrate that our model yields substantial improvements over the baselines (BiLSTM and BERT).
%R 10.18653/v1/2020.coling-main.282
%U https://aclanthology.org/2020.coling-main.282
%U https://doi.org/10.18653/v1/2020.coling-main.282
%P 3168-3178

Markdown (Informal)

[Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition](https://aclanthology.org/2020.coling-main.282) (Ruan et al., COLING 2020)

• Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition (Ruan et al., COLING 2020)

ACL

• Huibin Ruan, Yu Hong, Yang Xu, Zhen Huang, Guodong Zhou, and Min Zhang. 2020. Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition. In Proceedings of the 28th International Conference on Computational Linguistics, pages 3168–3178, Barcelona, Spain (Online). International Committee on Computational Linguistics.