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We propose BlackOut, an approximation algorithm to efficiently train massiverecurrent neural network language models (RNNLMs) with million wordvocabularies. BlackOut is motivated by using a discriminative loss, and wedescribe a new sampling strategy which significantly reduces computation whileimproving stability, sample efficiency, and rate of convergence. One way tounderstand BlackOut is to view it as an extension of the DropOut strategy tothe output layer, wherein we use a discriminative training loss and a weightedsampling scheme. We also establish close connections between BlackOut,importance sampling, and noise contrastive estimation (NCE). Our experiments,on the recently released one billion word language modeling benchmark,demonstrate scalability and accuracy of BlackOut; we outperform thestate-of-the art, and achieve the lowest perplexity scores on this dataset.Moreover, unlike other established methods which typically require GPUs or CPUclusters, we show that a carefully implemented version of BlackOut requiresonly 1-10 days on a single machine to train a RNNLM with a million wordvocabulary and billions of parameters on one billion words. Although wedescribe BlackOut in the context of RNNLM training, it can be used to anynetworks with large softmax output layers.

BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies