@inproceedings{yang-zhang-2018-ncrf,
    title = "{NCRF}++: An Open-source Neural Sequence Labeling Toolkit",
    author = "Yang, Jie  and
      Zhang, Yue",
    editor = "Liu, Fei  and
      Solorio, Thamar",
    booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P18-4013/",
    doi = "10.18653/v1/P18-4013",
    pages = "74--79",
    abstract = "This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++ is designed for quick implementation of different neural sequence labeling models with a CRF inference layer. It provides users with an inference for building the custom model structure through configuration file with flexible neural feature design and utilization. Built on PyTorch \url{http://pytorch.org/}, the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU. It also includes the implementations of most state-of-the-art neural sequence labeling models such as LSTM-CRF, facilitating reproducing and refinement on those methods."
}