Our experiments in CLIR and statistical MT indicate that selecting training data based on the above-mentioned approaches allows a significant reduction in training data while preserving about 90% of the performance. This result significantly surpasses the random selection approach, and it holds for both CLIR and MT. As expected, the difference increases as the subdomain becomes more specific. Our optimized criteria weights considerably outperform the uniform distribution baseline, as well as lexical similarity adaptation.
