Paper: Hierarchical Bayesian Domain Adaptation

ACL ID N09-1068
Title Hierarchical Bayesian Domain Adaptation
Venue Human Language Technologies
Session Main Conference
Year 2009

Multi-task learning is the problem of maxi- mizing the performance of a system across a number of related tasks. When applied to mul- tiple domains for the same task, it is similar to domain adaptation, but symmetric, rather than limited to improving performance on a target domain. We present a more principled, better performing model for this problem, based on the use of a hierarchical Bayesian prior. Each domain has its own domain-specific parame- ter for each feature but, rather than a constant prior over these parameters, the model instead links them via a hierarchical Bayesian global prior. This prior encourages the features to have similar weights across domains, unless there is good evidence to the contrary. We show that the method of (Daum´e III, 2007), which was presented as a si...