Training Restricted Boltzmann Machines on Word Observations
This addresses a bottleneck in natural language processing for researchers and practitioners by making RBM training more efficient on large vocabularies, though it is an incremental improvement over existing methods.
The paper tackled the computational difficulty of training Restricted Boltzmann Machines (RBMs) on high-dimensional word observations by introducing a new Markov chain Monte Carlo method that reduces complexity from linear to independent of vocabulary size, enabling training on larger vocabularies and achieving state-of-the-art results in sentiment classification.
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.