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How well do neural networks generalize? Even for grammar induction tasks,where the target generalization is fully known, previous works have left thequestion open, testing very limited ranges beyond the training set and usingdifferent success criteria. We provide a measure of neural networkgeneralization based on fully specified formal languages. Given a model and aformal grammar, the method assigns a generalization score representing how wella model generalizes to unseen samples in inverse relation to the amount of datait was trained on. The benchmark includes languages such as $a^nb^n$,$a^nb^nc^n$, $a^nb^mc^{n+m}$, and Dyck-1 and 2. We evaluate selectedarchitectures using the benchmark and find that networks trained with a MinimumDescription Length objective (MDL) generalize better and using less data thannetworks trained using standard loss functions. The benchmark is available athttps://github.com/taucompling/bliss.

Benchmarking Neural Network Generalization for Grammar Induction