MedNLI — A Natural Language Inference Dataset For The Clinical Domain

Dataset Description

	Links
Homepage:	Github.io
Repository:	Github
Paper:	arXiv
Leaderboard:	Papers with Code
Contact (Original Authors):	Alexey Romanov aromanov@cs.uml.edu, Chaitanya Shivade cshivade@us.ibm.com
Contact (Curator):	Artur Guimarães (artur.guimas@gmail.com)

Dataset Summary

`Natural Language Inference (NLI) is one of the critical tasks for understanding natural language. The objective of NLI is to determine if a given hypothesis can be inferred from a given premise. NLI systems have made significant progress over the years, and has gained popularity since the recent release of datasets such as the Stanford Natural Language Inference (SNLI) (Bowman et al. 2015) and Multi-NLI (Nangia et al. 2017).

We introduce MedNLI - a dataset annotated by doctors, performing a natural language inference task), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.`

Data Instances

Source Format

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Data Fields

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Data Splits

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Additional Information

Dataset Curators

Original Paper

• Alexey Romanov (aromanov@cs.uml.edu) - Department of Computer Science, University of Massachusetts Lowell
• Chaitanya Shivade (cshivade@us.ibm.com) - IBM Almaden Research Center

Huggingface Curator

• Artur Guimarães (artur.guimas@gmail.com) - INESC-ID / University of Lisbon - Instituto Superior Técnico

Licensing Information

CC BY-SA 4.0

Citation Information

@article{romanov2018lessons,
    title = {Lessons from Natural Language Inference in the Clinical Domain},
    url = {http://arxiv.org/abs/1808.06752},
    abstract = {State of the art models using deep neural networks have become very good in learning an accurate mapping from inputs to outputs. However, they still lack generalization capabilities in conditions that differ from the ones encountered during training. This is even more challenging in specialized, and knowledge intensive domains, where training data is limited. To address this gap, we introduce {MedNLI} - a dataset annotated by doctors, performing a natural language inference task ({NLI}), grounded in the medical history of patients. We present strategies to: 1) leverage transfer learning using datasets from the open domain, (e.g. {SNLI}) and 2) incorporate domain knowledge from external data and lexical sources (e.g. medical terminologies). Our results demonstrate performance gains using both strategies.},
    journaltitle = {arXiv:1808.06752 [cs]},
    author = {Romanov, Alexey and Shivade, Chaitanya},
    urldate = {2018-08-27},
    date = {2018-08-21},
    eprinttype = {arxiv},
    eprint = {1808.06752},
}

10.13026/C2RS98

Contributions

Thanks to araag2 for adding this dataset.