Paper: Domain Adaptation by Constraining Inter-Domain Variability of Latent Feature Representation

ACL ID P11-1007
Title Domain Adaptation by Constraining Inter-Domain Variability of Latent Feature Representation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We consider a semi-supervised setting for do- main adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative model with latent variables on the mixture of data from the source and target domains. Such a model would cluster features in both do- mains and ensure that at least some of the la- tent variables are predictive of the label on the source domain. The danger is that these pre- dictive clusters will consist of features specific to the source domain only and, consequently, a classifier relying on such clusters would per- form badly on the target domain. We in- troduce a constraint enforcing that marginal distributions of each cluster (i.e., each latent variable) do not vary significantly across do- mains. We show th...