Generalizing Outside the Training Set: When Can Neural Networks Learn Identity Effects?

Understand

Often in language and other areas of cognition, whether two components of an object are identical or not determine whether it is well formed.

• We call such constraints identity effects.
• When developing a system to learn well-formedness from examples, it is easy enough to build in an identify effect.
• But can identity effects be learned from the data without explicit guidance? We provide a simple framework in which we can rigorously prove that algorithms satisfying simple criteria cannot make the correct inference.