Structured Output Layer Neural Network Language Models for Speech Recognition

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Deep Learning: Methods and Applications (2014, background): “1: The SOUL–NNLM architecture with hierarchical structure in the output layers of the neural network [after [207], @IEEE].”
Sparse Non-negative Matrix Language Modeling (2015, unknown): “However, these techniques either come with a serious performance degradation (Le et al., 2013) or”
Incrementally Learning the Hierarchical Softmax Function for Neural Language Models (2017, background): “More semantics related trees can be built (Mnih and Hinton 2008; Le et al. 2013), and the same problem of tree change should be handled.”
From Feedforward to Recurrent LSTM Neural Networks for Language Modeling (2015, background): “In particular, previous work has concentrated on feedforward neural networks ([7], [8]), recurrent neural networks ([9]), and recurrent long short-term memory (LSTM) neural networks ([10]).”
Representation Learning: A Review and New Perspectives (2012, methodology): “Similar models have been applied in statistical machine translation (Schwenk et al., 2012; Le et al., 2013), improving perplexity and BLEU scores.”