AI

Sparse Non-negative Matrix Language Modeling: Maximum Entropy Flexibility on the Cheap

Abstract

We present a new method for estimating the sparse non-negative model (SNM) by using a small amount of held-out data and the multinomial loss that is natural for language modeling; we validate it experimentally against the previous estimation method which uses leave-one-out on training data and a binary loss function and show that it performs equally well. Being able to train on held-out data is very important in practical situations where training data is mismatched from held-out/test data. We find that fairly small amounts of held-out data (on the order of 30-70 thousand words) are sufficient for training the adjustment model, which is the only model component estimated using gradient descent; the bulk of model parameters are relative frequencies counted on training data.

A second contribution is a comparison between SNM and the related class of Maximum Entropy language models. While much cheaper computationally, we show that SNM achieves slightly better perplexity results for the same feature set and same speech recognition accuracy on voice search and short message dictation.