Direct Output Connection for a High-Rank Language Model
Sho Takase, Jun Suzuki, Masaaki Nagata · 2018
This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also from middle layers.Our proposed method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by Yang et al. (2018).The proposed method improves the current state-of-the-art language model and achieves the best score on the Penn Treebank and WikiText-2, which are the standard benchmark datasets.Moreover, we indicate our proposed method contributes to two application tasks: machine translation and headline generation.