R&M's network, however, was severely limited and did not generalize correctly to held-out data.
state-of-the art morphological generation networks used in NLP, built from the modern evolution of recurrent neural networks (RNNs) explored by Elman (1990) and others, solve the same problem almost perfectly (Cotterell et al., 2016).