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.

通过使用模糊性相对子句附加来扩展语言模型在多重同时有效解释的情况下的评估方法。我们比较了英语和西班牙语模型的表现，以展示 RNN LMs 中的非语言偏见优势重叠在英语而非西班牙语的句法结构上。因此，英语模型可能具有类似于人类的句法偏好，而在西班牙语上训练的模型无法获得类似的人类句法偏好。