Active Learning in Example-Based Machine Translation
In data-driven Machine Translation approaches, like Example-Based Machine Translation (EBMT) (Brown, 2000) and Statistical Machine Translation (Vogel et al., 2003), the quality of the translations produced depends on the amount of training data available. While more data is always useful, a large training corpus can slow down a machine translation system. We would like to selectively sample the huge corpus to obtain a sub-corpus of most informative sentence pairs that would lead to good quality translations. Reducing the amount of training data also enables one to easily port an MT system onto small devices that have less memory and storage capacity. In this paper, we propose using Active Learning strategies to sample the most informative sentence pairs. There has not been much progress in the application of active learning theory in machine translation due to the complexity of the translation models. We use a poolbased strategy to selectively sample instances from a parallel corpora which not only outperformed a random selector but also a previously used sampling strategy (Eck et al., 2005) in an EBMT framework (Brown, 2000) by about one BLEU point (Papineni et al., 2002).