  Psycholinguistic research suggests that humans may build a representation of
linguistic input that is 'good-enough' for the task at hand. This study
examines what architectural features make language models learn human-like
good-enough language processing. We focus on the number of layers and
self-attention heads in Transformers. We create a good-enough language
processing (GELP) evaluation dataset (7,680 examples), which is designed to
test the effects of two plausibility types, eight construction types, and three
degrees of memory cost on language processing. To annotate GELP, we first
conduct a crowdsourcing experiment whose design follows prior psycholinguistic
studies. Our model evaluation against the annotated GELP then reveals that the
full model as well as models with fewer layers and/or self-attention heads
exhibit a good-enough performance. This result suggests that models with
shallower depth and fewer heads can learn good-enough language processing.
