However, manually creating training data to match the test domain is not a preferred solution, because 1) sometimes the test domain is not known when training the model, and it could change from sentence to sentence; 2) even if the test domain is pre-determined, the resources required and slow turnaround in data collection process will still delay the system development process.
training data selection is widely used for domain adaptation in statistical machine translation (Zhao et al., 2004;Lü et al., 2007;Yasuda et al., 2008;Moore and Lewis, 2010;Axelrod et al., 2011;Duh et al., 2013;Axelrod et al., 2015).