Training Restricted Boltzmann Machines on Word Observations

TL;DR

This paper introduces a new training method for Restricted Boltzmann Machines that efficiently handles high-dimensional word data by using advanced MCMC operators, enabling training on large vocabularies and improving NLP task performance.

Contribution

The authors develop a novel MCMC approach that reduces computational complexity in training RBMs on word observations, allowing for larger vocabularies and better NLP task results.

Findings

Successfully trained RBMs on hundreds of millions of word n-grams

Achieved state-of-the-art results in sentiment classification

Improved NLP task performance using learned features

Abstract

The restricted Boltzmann machine (RBM) is a flexible tool for modeling complex data, however there have been significant computational difficulties in using RBMs to model high-dimensional multinomial observations. In natural language processing applications, words are naturally modeled by K-ary discrete distributions, where K is determined by the vocabulary size and can easily be in the hundreds of thousands. The conventional approach to training RBMs on word observations is limited because it requires sampling the states of K-way softmax visible units during block Gibbs updates, an operation that takes time linear in K. In this work, we address this issue by employing a more general class of Markov chain Monte Carlo operators on the visible units, yielding updates with computational complexity independent of K. We demonstrate the success of our approach by training RBMs on hundreds of millions of word n-grams using larger vocabularies than previously feasible and using the learned features to improve performance on chunking and sentiment classification tasks, achieving state-of-the-art results on the latter.