MedNLI for Shared Task at ACL BioNLP 2019

Natural language inference (NLI) is the task of determining whether a given
hypothesis can be inferred from a given premise. Also known as Recognizing
Textual Entailment (RTE), this task has enjoyed popularity among researchers
for a long time. However, almost all datasets for this task focused on open
domain data such as as news texts, blogs, and so on. To address this gap, the
[MedNLI](http://doi.org/10.13026/C2RS98) dataset was created or language
inference in the medical domain. MedNLI was a derived dataset with data
sourced from [MIMIC-III](http://doi.org/10.13026/C2XW26) v1.4. In order to
stimulate research for this problem, the
[MEDIQA](https://aclweb.org/aclwiki/BioNLP_Workshop#MEDIQA_2019) shared task
has been organized at BioNLP 2019. The dataset provided herein is a test set
of 405 premise hypothesis pairs for the NLI challenge at the MEDIQA shared
task. Participants of the shared task are expected to use the MedNLI data for
development of their models and this dataset will be used as an unseen dataset
for scoring each participant submission.