  Language models (LMs) trained on large quantities of text have been claimed
to acquire abstract linguistic representations. Our work tests the robustness
of these abstractions by focusing on the ability of LMs to learn interactions
between different linguistic representations. In particular, we utilized
stimuli from psycholinguistic studies showing that humans can condition
reference (i.e. coreference resolution) and syntactic processing on the same
discourse structure (implicit causality). We compared both transformer and long
short-term memory LMs to find that, contrary to humans, implicit causality only
influences LM behavior for reference, not syntax, despite model representations
that encode the necessary discourse information. Our results further suggest
that LM behavior can contradict not only learned representations of discourse
but also syntactic agreement, pointing to shortcomings of standard language
modeling.
