Highly regularized LSTMs achieve impressive results on several benchmark datasets in language modeling. We propose a new regularization method based on decoding the last token in the context using the predicted distribution of the next token. This biases the model towards retaining more contextual information, in turn improving its ability to predict the next token. With negligible overhead in the number of parameters and training time, our Past Decode Regularization (PDR) method achieves a word level perplexity of 55.6 on the Penn Treebank and 63.5 on the WikiText-2 datasets using a single softmax. We also show gains by using PDR in combination with a mixture-of-softmaxes, achieving a word level perplexity of 53.8 and 60.5 on these datasets. In addition, our method achieves 1.169 bits-per-character on the Penn Treebank Character dataset for character level language modeling. These results constitute a new state-of-the-art in their respective settings.
Decoding the last token in the context using the predicted next token distribution acts as a regularizer and improves language modeling. The authors introduce the idea of past decoding for the purpose of regularization for improved perplexity on Penn Treebank Proposes an additional loss term to use when training an LSTM LM and shows that by adding this loss term they can achieve SOTA perplexity on a number of LM benchmarks. Suggests a new regularization technique which can be added on top of those used in AWD-LSTM of Merity et al. (2017) with little overhead.