  Recurrent neural networks (RNNs), such as long short-term memory networks
(LSTMs), serve as a fundamental building block for many sequence learning
tasks, including machine translation, language modeling, and question
answering. In this paper, we consider the specific problem of word-level
language modeling and investigate strategies for regularizing and optimizing
LSTM-based models. We propose the weight-dropped LSTM which uses DropConnect on
hidden-to-hidden weights as a form of recurrent regularization. Further, we
introduce NT-ASGD, a variant of the averaged stochastic gradient method,
wherein the averaging trigger is determined using a non-monotonic condition as
opposed to being tuned by the user. Using these and other regularization
strategies, we achieve state-of-the-art word level perplexities on two data
sets: 57.3 on Penn Treebank and 65.8 on WikiText-2. In exploring the
effectiveness of a neural cache in conjunction with our proposed model, we
achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and
52.0 on WikiText-2.
