Multi-Domain Learning: When Do Domains Matter?

We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. First, many multidomain learning algorithms resemble ensemble learning algorithms. (1) Are multi-domain learning improvements the result of ensemble learning effects? Second, these algorithms are traditionally evaluated in a balanced class label setting, although in practice many multidomain settings have domain-specific class label biases. When multi-domain learning is applied to these settings, (2) are multidomain methods improving because they capture domain-specific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multi-domain learning, and it suggests some important open questions for improving beyond the current state of the art.