  Rapid progress in machine learning for natural language processing has the
potential to transform debates about how humans learn language. However, the
learning environments and biases of current artificial learners and humans
diverge in ways that weaken the impact of the evidence obtained from learning
simulations. For example, today's most effective neural language models are
trained on roughly one thousand times the amount of linguistic data available
to a typical child. To increase the relevance of learnability results from
computational models, we need to train model learners without significant
advantages over humans. If an appropriate model successfully acquires some
target linguistic knowledge, it can provide a proof of concept that the target
is learnable in a hypothesized human learning scenario. Plausible model
learners will enable us to carry out experimental manipulations to make causal
inferences about variables in the learning environment, and to rigorously test
poverty-of-the-stimulus-style claims arguing for innate linguistic knowledge in
humans on the basis of speculations about learnability. Comparable experiments
will never be possible with human subjects due to practical and ethical
considerations, making model learners an indispensable resource. So far,
attempts to deprive current models of unfair advantages obtain sub-human
results for key grammatical behaviors such as acceptability judgments. But
before we can justifiably conclude that language learning requires more prior
domain-specific knowledge than current models possess, we must first explore
non-linguistic inputs in the form of multimodal stimuli and multi-agent
interaction as ways to make our learners more efficient at learning from
limited linguistic input.
