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We introduce LL-RNNs (Log-Linear RNNs), an extension of Recurrent NeuralNetworks that replaces the softmax output layer by a log-linear output layer,of which the softmax is a special case. This conceptually simple move has twomain advantages. First, it allows the learner to combat training data sparsityby allowing it to model words (or more generally, output symbols) as complexcombinations of attributes without requiring that each combination is directlyobserved in the training data (as the softmax does). Second, it permits theinclusion of flexible prior knowledge in the form of a priori specified modularfeatures, where the neural network component learns to dynamically control theweights of a log-linear distribution exploiting these features.  We conduct experiments in the domain of language modelling of French, thatexploit morphological prior knowledge and show an important decrease inperplexity relative to a baseline RNN.  We provide other motivating iillustrations, and finally argue that thelog-linear and the neural-network components contribute complementary strengthsto the LL-RNN: the LL aspect allows the model to incorporate rich priorknowledge, while the NN aspect, according to the "representation learning"paradigm, allows the model to discover novel combination of characteristics.

Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge