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        <title>A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks</title>
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        <namePart type="given">Kazuma</namePart>
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    <name type="personal">
        <namePart type="given">Yoshimasa</namePart>
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    <name type="personal">
        <namePart type="given">Richard</namePart>
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            <title>Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Martha</namePart>
            <namePart type="family">Palmer</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. Higher layers include shortcut connections to lower-level task predictions to reflect linguistic hierarchies. We use a simple regularization term to allow for optimizing all model weights to improve one task’s loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end model obtains state-of-the-art or competitive results on five different tasks from tagging, parsing, relatedness, and entailment tasks.</abstract>
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        <date>2017-09</date>
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%0 Conference Proceedings
%T A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
%A Hashimoto, Kazuma
%A Xiong, Caiming
%A Tsuruoka, Yoshimasa
%A Socher, Richard
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F hashimoto-etal-2017-joint
%X Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. Higher layers include shortcut connections to lower-level task predictions to reflect linguistic hierarchies. We use a simple regularization term to allow for optimizing all model weights to improve one task’s loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end model obtains state-of-the-art or competitive results on five different tasks from tagging, parsing, relatedness, and entailment tasks.
%R 10.18653/v1/D17-1206
%U https://aclanthology.org/D17-1206
%U https://doi.org/10.18653/v1/D17-1206
%P 1923-1933

Markdown (Informal)

[A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks](https://aclanthology.org/D17-1206) (Hashimoto et al., EMNLP 2017)

• A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks (Hashimoto et al., EMNLP 2017)

ACL

• Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017. A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1923–1933, Copenhagen, Denmark. Association for Computational Linguistics.