Multi-Domain Learning: When Do Domains Matter?
Mahesh Joshi, Mark H. Dredze, William W. Cohen · 2013
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 label setting, although in practice many multidomain settings have domain-specific label biases. When multi-domain learning is applied to these settings, (2) are multi-domain methods improving because they capture domainspecific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multidomain learning, and it suggests some important open questions for improving beyond the current state of the art. 1