Recently, variants of neural networks for computational linguistics have been proposed and successfully applied to neural language modeling and neural machine translation. These neural models can leverage knowledge from massive corpora but they are extremely slow as they predict candidate words from a large vocabulary during training and inference. As an alternative to gradient approximation and softmax with class decomposition, we explore the tree-based hierarchical softmax method and reform its architecture, making it compatible with modern GPUs and introducing a compact treebased loss function. When combined with several word hierarchical clustering algorithms, improved performance is achieved in language modelling task with intrinsic evaluation criterions on PTB, WikiText-2 and WikiText-103 datasets.
Recently, REF reform the tree-based structure to make it more efficient on GPUs.
Exploration of tree-based hierarchical softmax for recurrent language models
IJCAI
Computer Science