Paper: Online Active Learning for Cost Sensitive Domain Adaptation

ACL ID W13-3501
Title Online Active Learning for Cost Sensitive Domain Adaptation
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2013

Active learning and domain adaptation are both important tools for reducing labeling effort to learn a good supervised model in a target domain. In this paper, we inves- tigate the problem of online active learn- ing within a new active domain adapta- tion setting: there are insufficient labeled data in both source and target domains, but it is cheaper to query labels in the source domain than in the target domain. Given a total budget, we develop two cost- sensitive online active learning methods, a multi-view uncertainty-based method and a multi-view disagreement-based method, to query the most informative instances from the two domains, aiming to learn a good prediction model in the target do- main. Empirical studies on the tasks of cross-domain sentiment classification of Amazon produc...