This paper aims at effective use of training data by extracting sentences from large generaldomain corpora to adapt statistical machine translation systems to domain-specific data. We regard this task as a problem of filtering training sentences with respect to the target domain 1 via different similarity metrics. Thus, we give new insights into when data selection model can best benefit the in-domain translation. Based on the investigation of the state-ofthe-art similarity metrics, we propose edit distance as a new data selection criterion for this topic. To evaluate this proposal, we compare it with other methods on a large dataset. Comparative experiments are conducted on Chinese-English travel dialog domain and the results indicate that the proposed approach achieves a significant improvement over the baseline system (+4.36 BLEU) as well as the best rival model (+1.23 BLEU) using a much smaller training subset. This study may have a significant impact on mining very large corpora in a computationally-limited environment.
['Longyue Wang', 'Derek F. Wong', 'Lidia S. Chao', 'Junwen Xing', 'Yi Lu', 'Isabel Trancoso']
Edit Distance: A New Data Selection Criterion for Domain Adaptation in SMT