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        <title>MCapsNet: Capsule Network for Text with Multi-Task Learning</title>
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    <name type="personal">
        <namePart type="given">Liqiang</namePart>
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    <name type="personal">
        <namePart type="given">Honglun</namePart>
        <namePart type="family">Zhang</namePart>
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    <name type="personal">
        <namePart type="given">Wenqing</namePart>
        <namePart type="family">Chen</namePart>
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        <namePart type="given">Yongkun</namePart>
        <namePart type="family">Wang</namePart>
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    <name type="personal">
        <namePart type="given">Yaohui</namePart>
        <namePart type="family">Jin</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>Multi-task learning has an ability to share the knowledge among related tasks and implicitly increase the training data. However, it has long been frustrated by the interference among tasks. This paper investigates the performance of capsule network for text, and proposes a capsule-based multi-task learning architecture, which is unified, simple and effective. With the advantages of capsules for feature clustering, proposed task routing algorithm can cluster the features for each task in the network, which helps reduce the interference among tasks. Experiments on six text classification datasets demonstrate the effectiveness of our models and their characteristics for feature clustering.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1486</identifier>
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%0 Conference Proceedings
%T MCapsNet: Capsule Network for Text with Multi-Task Learning
%A Xiao, Liqiang
%A Zhang, Honglun
%A Chen, Wenqing
%A Wang, Yongkun
%A Jin, Yaohui
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xiao-etal-2018-mcapsnet
%X Multi-task learning has an ability to share the knowledge among related tasks and implicitly increase the training data. However, it has long been frustrated by the interference among tasks. This paper investigates the performance of capsule network for text, and proposes a capsule-based multi-task learning architecture, which is unified, simple and effective. With the advantages of capsules for feature clustering, proposed task routing algorithm can cluster the features for each task in the network, which helps reduce the interference among tasks. Experiments on six text classification datasets demonstrate the effectiveness of our models and their characteristics for feature clustering.
%R 10.18653/v1/D18-1486
%U https://aclanthology.org/D18-1486
%U https://doi.org/10.18653/v1/D18-1486
%P 4565-4574

Markdown (Informal)

[MCapsNet: Capsule Network for Text with Multi-Task Learning](https://aclanthology.org/D18-1486) (Xiao et al., EMNLP 2018)

• MCapsNet: Capsule Network for Text with Multi-Task Learning (Xiao et al., EMNLP 2018)

ACL

• Liqiang Xiao, Honglun Zhang, Wenqing Chen, Yongkun Wang, and Yaohui Jin. 2018. MCapsNet: Capsule Network for Text with Multi-Task Learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4565–4574, Brussels, Belgium. Association for Computational Linguistics.