A Fast Variational Approach for Learning Markov Random Field Language Models

Abstract

Language modelling is a fundamental building block of natural language processing. However, in practice the size of the vocabulary limits thedistributions applicable for this task: specifically, one has to either resort to local optimization methods, such as those used in neural language models, or work with heavily constrained distributions. In this work, we take a step towards overcoming these difficulties. We present a method for global-likelihood optimization of a Markov random field language model exploiting long-range contexts in time independent of the corpus size. We take a variational approach to optimizing the likelihood and exploit underlying symmetries to greatly simplify learning. We demonstrate the efficiency of this method both for language modelling and for part-of-speech tagging.

Publication
Proceedings of the 32nd International Conference on Machine Learning (ICML)
Yacine Jernite
Yacine Jernite
PhD student

Research Scientist, Hugging Face

David Sontag
David Sontag
Associate Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

Related