  Understanding language requires grasping not only the overtly stated content,
but also making inferences about things that were left unsaid. These inferences
include presuppositions, a phenomenon by which a listener learns about new
information through reasoning about what a speaker takes as given.
Presuppositions require complex understanding of the lexical and syntactic
properties that trigger them as well as the broader conversational context. In
this work, we introduce the Naturally-Occurring Presuppositions in English
(NOPE) Corpus to investigate the context-sensitivity of 10 different types of
presupposition triggers and to evaluate machine learning models' ability to
predict human inferences. We find that most of the triggers we investigate
exhibit moderate variability. We further find that transformer-based models
draw correct inferences in simple cases involving presuppositions, but they
fail to capture the minority of exceptional cases in which human judgments
reveal complex interactions between context and triggers.
