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Multi-Task Deep Neural Networks for Natural Language Understanding
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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) 1 .We also demonstrate using the SNLI and Sc-iTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations.The code and pre-trained models are publicly available at https://github.com/namisan/mt-dnn. * Equal Contribution. 1 As of February 25, 2019 on the latest GLUE test set.
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Metadata
- Authors
- Liu, X., He, P., Chen, W., & Gao, J.
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 4487-4496
- OpenAlex
- W2963854351
- Cited by
- 1,154
- References
- 10
- FWCI
- 80.23
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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