Translation Quality-Based Supplementary Data Selection by Incremental Update of Translation Models

Supplementary data selection from out-of-domain or related-domain data is a well established technique in domain adaptation of statistical machine translation. The selection criteria for such data are mostly based on measures of similarity with available in-domain data, but not directly in terms of translation quality. In this paper, we present a technique for selecting supplementary data to improve translation performance, directly in terms of translation quality, measured by automatic evaluation metric scores. Batches of data selected from out-of-domain corpora are incrementally added to an existing baseline system and evaluated in terms of translation quality on a development set. A batch is selected only if its inclusion improves translation quality. To assist the process, we present a novel translation model merging technique that allows rapid retraining of the translation models with incremental data. When incorporated into the ‘in-domain’ translation models, the final cumulatively selected datasets are found to provide statistically significant improvements for a number of different supplementary datasets. Furthermore, the translation model merging technique is found to perform on a par with state-of-the-art methods of phrase-table combination.