For example, in the case of 10000, the CG allows both ten thousand and one o o o o, and given the choice, the neural model picks the latter: left to its own devices, the neural model produced one thousand.
rather, neural network solutions in general tend to make unrecoverable errors, and for these we have argued that using trainable finite-state covering grammars is a reasonable approach, but we continue to look for ways to improve covering grammar training and coverage.