Understanding natural language requires common sense, one aspect of which is
the ability to discern the plausibility of events. While distributional models
-- most recently pre-trained, Transformer language models -- have demonstrated
improvements in modeling event plausibility, their performance still falls
short of humans'. In this work, we show that Transformer-based plausibility
models are markedly inconsistent across the conceptual classes of a lexical
hierarchy, inferring that "a person breathing" is plausible while "a dentist
breathing" is not, for example. We find this inconsistency persists even when
models are softly injected with lexical knowledge, and we present a simple
post-hoc method of forcing model consistency that improves correlation with
human plausibility judgements.

本文研究证明对自然语言的理解需要常识，其中之一是辨别事件的可信度。这篇论文探讨了基于 Transformer 的可信度模型在词汇等级概念类别方面的不一致性，并提出了强制模型一致性的后续方法，以提高与人类可信度判断的相关性。