@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",
    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 https://github.com/namisan/mt-dnn.",
}