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        "description": "Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop\nwhen dealing with domain text, especially for a domain with lots of special terms and diverse\nwriting styles, such as the biomedical domain. However, building domain-specific CWS requires\nextremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant\nknowledge from high resource to low resource domains. Extensive experiments show that our mode\nachieves consistently higher accuracy than the single-task CWS and other transfer learning\nbaselines, especially when there is a large disparity between source and target domains.\n\nThis dataset is the accompanied medical Chinese word segmentation (CWS) dataset.\nThe tags are in BIES scheme.\n\nFor more details see https://www.aclweb.org/anthology/C18-1307/\n",
        "citation": "@inproceedings{xing2018adaptive,\n  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},\n  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},\n  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},\n  pages={3619--3630},\n  year={2018}\n}\n",
        "homepage": "https://www.aclweb.org/anthology/C18-1307/",
        "license": "",
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