Paper: Multi-Domain Learning: When Do Domains Matter?

ACL ID D12-1119
Title Multi-Domain Learning: When Do Domains Matter?
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

We present a systematic analysis of exist- ing multi-domain learning approaches with re- spect to two questions. First, many multi- domain learning algorithms resemble ensem- ble learning algorithms. (1) Are multi-domain learning improvements the result of ensemble learning effects? Second, these algorithms are traditionally evaluated in a balanced class la- bel setting, although in practice many multi- domain settings have domain-specific class label biases. When multi-domain learning is applied to these settings, (2) are multi- domain methods improving because they cap- ture domain-specific class biases? An under- standing 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...