  All known natural language determiners are conservative. Psycholinguistic
experiments indicate that children exhibit a corresponding learnability bias
when faced with the task of learning new determiners. However, recent work
indicates that this bias towards conservativity is not observed during the
training stage of artificial neural networks. In this work, we investigate
whether the learnability bias exhibited by children is in part due to the
distribution of quantifiers in natural language. We share results of five
experiments, contrasted by the distribution of conservative vs.
non-conservative determiners in the training data. We demonstrate that the
aquisitional issues with non-conservative quantifiers can not be explained by
the distribution of natural language data, which favors conservative
quantifiers. This finding indicates that the bias in language acquisition data
might be innate or representational.
