We're not in Kansas anymore: detecting domain changes in streams

Domain adaptation, the problem of adapting a natural language processing system trained in one domain to perform well in a different domain, has received significant attention. This paper addresses an important problem for deployed systems that has received little attention - detecting when such adaptation is needed by a system operating in the wild, i.e., performing classification over a stream of unlabeled examples. Our method uses A-distance, a metric for detecting shifts in data streams, combined with classification margins to detect domain shifts. We empirically show effective domain shift detection on a variety of data sets and shift conditions.