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

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.

• Speech Processing

• Machine Intelligence