  Crowdworker-constructed natural language inference (NLI) datasets have been
found to contain statistical artifacts associated with the annotation process
that allow hypothesis-only classifiers to achieve better-than-random
performance (Poliak et al., 2018; Gururanganet et al., 2018; Tsuchiya, 2018).
We investigate whether MedNLI, a physician-annotated dataset with premises
extracted from clinical notes, contains such artifacts (Romanov and Shivade,
2018). We find that entailed hypotheses contain generic versions of specific
concepts in the premise, as well as modifiers related to responsiveness,
duration, and probability. Neutral hypotheses feature conditions and behaviors
that co-occur with, or cause, the condition(s) in the premise. Contradiction
hypotheses feature explicit negation of the premise and implicit negation via
assertion of good health. Adversarial filtering demonstrates that performance
degrades when evaluated on the difficult subset. We provide partition
information and recommendations for alternative dataset construction strategies
for knowledge-intensive domains.
