Representation learning is a promising technique for discovering features that allow supervised classifiers to generalize from a source domain dataset to arbitrary new domains. We present a novel, formal statement of the representation learning task. We argue that because the task is computationally intractable in general, it is important for a representation learner to be able to incorporate expert knowledge during its search for helpful features. Leveraging the Posterior Regularization framework, we develop an architecture for incorporating biases into representation learning. We investigate three types of biases, and experiments on two domain adaptation tasks show that our biased learners identify significantly better sets of features than unbiased learners, resulting in a relative reduction in error of more than 16% for both tasks, with respect to existing state-of-the-art representation learning techniques.
REF argue that finding an optimal feature representation is computationally intractable and propose a new framework that allows prior knowledge to be integrated into representation learning.
Biased Representation Learning for Domain Adaptation
EMNLP
Computer Science