@inproceedings{liu-etal-2020-microsoft,
title = "The {M}icrosoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding",
author = "Liu, Xiaodong and
Wang, Yu and
Ji, Jianshu and
Cheng, Hao and
Zhu, Xueyun and
Awa, Emmanuel and
He, Pengcheng and
Chen, Weizhu and
Poon, Hoifung and
Cao, Guihong and
Gao, Jianfeng",
editor = "Celikyilmaz, Asli and
Wen, Tsung-Hsien",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-demos.16",
doi = "10.18653/v1/2020.acl-demos.16",
pages = "118--126",
abstract = "We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at \url{https://github.com/namisan/mt-dnn}.",
}
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<abstract>We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn.</abstract>
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%0 Conference Proceedings
%T The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
%A Liu, Xiaodong
%A Wang, Yu
%A Ji, Jianshu
%A Cheng, Hao
%A Zhu, Xueyun
%A Awa, Emmanuel
%A He, Pengcheng
%A Chen, Weizhu
%A Poon, Hoifung
%A Cao, Guihong
%A Gao, Jianfeng
%Y Celikyilmaz, Asli
%Y Wen, Tsung-Hsien
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-microsoft
%X We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn.
%R 10.18653/v1/2020.acl-demos.16
%U https://aclanthology.org/2020.acl-demos.16
%U https://doi.org/10.18653/v1/2020.acl-demos.16
%P 118-126
Markdown (Informal)
[The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding](https://aclanthology.org/2020.acl-demos.16) (Liu et al., ACL 2020)
ACL
- Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, and Jianfeng Gao. 2020. The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 118–126, Online. Association for Computational Linguistics.