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Measuring and Improving Consistency in Pretrained Language Models

2021-02-01 17:48:42
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, Yoav Goldberg
arXiv_CL
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2102.01017

PDF

https://arxiv.org/pdf/2102.01017.pdf

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