  Recurrent Neural Networks (RNNs) are theoretically Turing-complete and
established themselves as a dominant model for language processing. Yet, there
still remains an uncertainty regarding their language learning capabilities. In
this paper, we empirically evaluate the inductive learning capabilities of Long
Short-Term Memory networks, a popular extension of simple RNNs, to learn simple
formal languages, in particular $a^nb^n$, $a^nb^nc^n$, and $a^nb^nc^nd^n$. We
investigate the influence of various aspects of learning, such as training data
regimes and model capacity, on the generalization to unobserved samples. We
find striking differences in model performances under different training
settings and highlight the need for careful analysis and assessment when making
claims about the learning capabilities of neural network models.
