Improved Language Modeling by Decoding the Past

TL;DR

This paper introduces Past Decode Regularization (PDR), a novel method that improves language modeling by encouraging models to retain more contextual information through decoding the last token, leading to state-of-the-art results.

Contribution

The paper proposes PDR, a simple yet effective regularization technique that enhances language models' ability to utilize context without significant computational overhead.

Findings

Achieves state-of-the-art perplexity on Penn Treebank and WikiText-2 datasets.

Improves character-level language modeling performance.

Enhances language model accuracy when combined with mixture-of-softmaxes.

Abstract

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.