- Rashmi Gangadharaiah, Ralf D. Brown, J. Carbonell. 2009. Active Learning in Example-Based Machine Translation. Abstract: In data-driven Machine Translation ap-proaches, like Example-Based MachineTranslation (EBMT) (Brown, 2000) andStatistical Machine Translation (Vogel etal., 2003), the quality of the translationsproduced depends on the amount of train-ing data available. While more data is al-ways useful, a large training corpus canslow down a machine translation system.We would like to selectively sample thehugecorpustoobtainasub-corpusofmostinformative sentence pairs that would leadto good quality translations. Reducing theamount of training data also enables oneto easily port an MT system onto smalldevices that have less memory and stor-age capacity. In this paper, we proposeusingActiveLearningstrategiestosamplethemostinformativesentencepairs. Therehas not been much progress in the ap-plication of active learning theory in ma-chine translation due to the complexity ofthe translation models. We use a pool-based strategy to selectively sample in-stances from a parallel corpora which notonly outperformed a random selector butalso a previously used sampling strategy(Eck et al., 2005) in an EBMT framework(Brown, 2000) by about one BLEU point(Papineni et al., 2002).
