  Consistency of a model -- that is, the invariance of its behavior under
meaning-preserving alternations in its input -- is a highly desirable property
in natural language processing. In this paper we study the question: Are
Pretrained Language Models (PLMs) consistent with respect to factual knowledge?
To this end, we create ParaRel, a high-quality resource of cloze-style query
English paraphrases. It contains a total of 328 paraphrases for thirty-eight
relations. Using ParaRel, we show that the consistency of all PLMs we
experiment with is poor -- though with high variance between relations. Our
analysis of the representational spaces of PLMs suggests that they have a poor
structure and are currently not suitable for representing knowledge in a robust
way. Finally, we propose a method for improving model consistency and
experimentally demonstrate its effectiveness.
