Abstract
Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are
widely used because they are expressive and are easy to train. Our interest
lies in empirically evaluating the expressiveness and the learnability of LSTMs
in the sequence-to-sequence regime by training them to evaluate short computer
programs, a domain that has traditionally been seen as too complex for neural
networks. We consider a simple class of programs that can be evaluated with a
single left-to-right pass using constant memory. Our main result is that LSTMs
can learn to map the character-level representations of such programs to their
correct outputs. Notably, it was necessary to use curriculum learning, and
while conventional curriculum learning proved ineffective, we developed a new
variant of curriculum learning that improved our networks' performance in all
experimental conditions. The improved curriculum had a dramatic impact on an
addition problem, making it possible to train an LSTM to add two 9-digit
numbers with 99% accuracy.
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