@inproceedings{chopra-etal-2019-msit,
    title = "{MSIT}{\_}{SRIB} at {MEDIQA} 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.",
    author = "Chopra, Sahil  and
      Gupta, Ankita  and
      Kaushik, Anupama",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W19-5052/",
    doi = "10.18653/v1/W19-5052",
    pages = "488--492",
    abstract = "In this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge. Bio-MTDNN utilizes ``transfer learning'' based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results."
}