  Strong inductive biases allow children to learn in fast and adaptable ways.
Children use the mutual exclusivity (ME) bias to help disambiguate how words
map to referents, assuming that if an object has one label then it does not
need another. In this paper, we investigate whether or not standard neural
architectures have an ME bias, demonstrating that they lack this learning
assumption. Moreover, we show that their inductive biases are poorly matched to
lifelong learning formulations of classification and translation. We
demonstrate that there is a compelling case for designing neural networks that
reason by mutual exclusivity, which remains an open challenge.

Computation and Language;Machine Learning