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.
@cite_0: A striking cross-linguistic generalisation about the semantics of determiners is that they never express non-conservative relations. To account for this one might hypothesise that the mechanisms underlying human language acquisition are unsuited to non-conservative determiner meanings. We present experimental evidence that 4- and 5-year-olds fail to learn a novel non-conservative determiner but succeed in learning a comparable conservative determiner, consistent with the learnability hypothesis.
investigate the learnability of semantic universals from a computational point of view. In their study, they train a LSTM network @cite_0 to perform a related sequence classification task: the input to the network is a sequence in which each element represents a set-theoretic model and a quantifier, and the output are two possible truth-values given the input. The model is a sequence of entities represented by one-hot encodings of four zones in a Venn diagram: A @math B, A @math B, B @math A, and @math . For example, an entity in A @math B is encoded in a vector @math 0,1,0,0 @math . Two encoded quantifiers are used in each experiment: one exhibiting the semantic universal, e.g. the conservative ( @math A @math B @math 0), and one not exhibiting the universal, e.g. the non-conservative ( @math B @math A @math 0).