Hierarchical Bayesian Domain Adaptation

Jenny Rose Finkel, Christopher D. Manning

Abstract: Multi-task learning is the problem of maximizing the performance of a system across a number of related tasks. When applied to multiple 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 parameter 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 (Daume III, 2007), which was presented as a simple "preprocessing step," is actually equivalent, except our representation explicitly separates hyperparameters which were tied in his work. We demonstrate that allowing different values for these hyperparameters significantly improves performance over both a strong baseline and (Daume III, 2007) within both a conditional random field sequence model for named entity recognition and a discriminatively trained dependency parser.