  A standard approach to evaluating language models analyzes how models assign
probabilities to valid versus invalid syntactic constructions (i.e. is a
grammatical sentence more probable than an ungrammatical sentence). Our work
uses ambiguous relative clause attachment to extend such evaluations to cases
of multiple simultaneous valid interpretations, where stark grammaticality
differences are absent. We compare model performance in English and Spanish to
show that non-linguistic biases in RNN LMs advantageously overlap with
syntactic structure in English but not Spanish. Thus, English models may appear
to acquire human-like syntactic preferences, while models trained on Spanish
fail to acquire comparable human-like preferences. We conclude by relating
these results to broader concerns about the relationship between comprehension
(i.e. typical language model use cases) and production (which generates the
training data for language models), suggesting that necessary linguistic biases
are not present in the training signal at all.
