Improved Language Modeling by Decoding the Past
This work addresses the challenge of enhancing contextual retention in language models for NLP applications, representing an incremental improvement over existing regularization techniques.
The paper tackled the problem of improving language modeling by proposing a new regularization method called Past Decode Regularization (PDR), which biases models to retain more contextual information, resulting in state-of-the-art perplexity scores such as 55.6 on Penn Treebank and 63.5 on WikiText-2.
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.