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        <title>Multi-Task Deep Neural Networks for Natural Language Understanding</title>
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        <namePart type="given">Xiaodong</namePart>
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        <namePart type="given">Pengcheng</namePart>
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        <namePart type="given">Weizhu</namePart>
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    <name type="personal">
        <namePart type="given">Jianfeng</namePart>
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    <abstract>In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1441</identifier>
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%0 Conference Proceedings
%T Multi-Task Deep Neural Networks for Natural Language Understanding
%A Liu, Xiaodong
%A He, Pengcheng
%A Chen, Weizhu
%A Gao, Jianfeng
%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 liu-etal-2019-multi
%X In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations to help adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement) as of February 25, 2019 on the latest GLUE test set. We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. Our code and pre-trained models will be made publicly available.
%R 10.18653/v1/P19-1441
%U https://aclanthology.org/P19-1441
%U https://doi.org/10.18653/v1/P19-1441
%P 4487-4496

Markdown (Informal)

[Multi-Task Deep Neural Networks for Natural Language Understanding](https://aclanthology.org/P19-1441) (Liu et al., ACL 2019)

• Multi-Task Deep Neural Networks for Natural Language Understanding (Liu et al., ACL 2019)

ACL

• Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019. Multi-Task Deep Neural Networks for Natural Language Understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4487–4496, Florence, Italy. Association for Computational Linguistics.