Paper: Variational Inference for Structured NLP Models

ACL ID N12-4005
Title Variational Inference for Structured NLP Models
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Tutorial Abstracts
Year 2012

Historically, key breakthroughs in structured NLP models, such as chain CRFs or PCFGs, have relied on imposing careful constraints on the locality of features in order to permit efficient dynamic programming for computing expectations or finding the highest- scoring structures. However, as modern structured models become more complex and seek to incorporate longer-range features, it is more and more often the case that performing exact inference is impossible (or at least impractical) and it is necessary to resort to some sort of approximation technique, such as beam search, pruning, or sampling. In the NLP community, one increasingly popular approach is the use of variational methods for computing approximate distributions.