Adaptive Input Representations for Neural Language Modeling

We introduce adaptive input representations for neural language modeling which extend the adaptive softmax of Grave et al. (2017) to input representations of variable capacity. There are several choices on how to factorize the input and output layers, and whether to model words, characters or sub-word units. We perform a systematic comparison of popular choices for a self-attentional architecture. Our experiments show that models equipped with adaptive embeddings are more than twice as fast to train than the popular character input CNN while having a lower number of parameters. We achieve a new state of the art on the WikiText-103 benchmark of 20.51 perplexity, improving the next best known result by 8.7 perplexity. On the Billion word benchmark, we achieve a state of the art of 24.14 perplexity.