Diverse Embedding Neural Network Language Models (1412.7063v5)

Abstract: We propose Diverse Embedding Neural Network (DENN), a novel architecture for LLMs (LMs). A DENNLM projects the input word history vector onto multiple diverse low-dimensional sub-spaces instead of a single higher-dimensional sub-space as in conventional feed-forward neural network LMs. We encourage these sub-spaces to be diverse during network training through an augmented loss function. Our LLMing experiments on the Penn Treebank data set show the performance benefit of using a DENNLM.