  While pretrained language models ("LM") have driven impressive gains over
morpho-syntactic and semantic tasks, their ability to model discourse and
pragmatic phenomena is less clear. As a step towards a better understanding of
their discourse modelling capabilities, we propose a sentence intrusion
detection task. We examine the performance of a broad range of pretrained LMs
on this detection task for English. Lacking a dataset for the task, we
introduce INSteD, a novel intruder sentence detection dataset, containing
170,000+ documents constructed from English Wikipedia and CNN news articles.
Our experiments show that pretrained LMs perform impressively in in-domain
evaluation, but experience a substantial drop in the cross-domain setting,
indicating limited generalisation capacity. Further results over a novel
linguistic probe dataset show that there is substantial room for improvement,
especially in the cross-domain setting.
