- Noam M. Shazeer
- Joris Pelemans
- Ciprian Chelba
Abstract
We present a novel family of language model (LM) estimation techniques named Sparse Non-negative Matrix (SNM) estimation.
A first set of experiments empirically evaluating these techniques on the One Billion Word Benchmark [3] shows that with skip-gram features SNMLMs are able to match the state-of-the art recurrent neural network (RNN) LMs; combining the two modeling techniques yields the best known result on the benchmark.
The computational advantages of SNM over both maximum entropy and RNNLM estimation are probably its main strength, promising an approach that has the same flexibility in combining arbitrary features effectively and yet should scale to very large amounts of data as gracefully as n-gram LMs do.
Research Areas
Learn more about how we do research
We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work