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Improving domain-specific SMT for low-resourced languages using data from different domains

This paper evaluates the impact of different types of data sources in developing a domain-specific statistical machine translation (SMT) system for the domain of official government letters, between the low-resourced language pair Sinhala and Tamil. The baseline was built with a small in-domain parallel data set containing official government letters. The translation system was evaluated with two different test datasets. Test data from the same sources as training and tuning gave a higher score due to over-fitting, while the test data from a different source resulted in a considerably lower score. With the motive to improve translation, more data was collected from, (a) different government sources other than official letters (pseudo in-domain), and (b) online sources such as blogs, news and wiki dumps (out-domain). Use of pseudo in-domain data showed an improvement for both the test sets as the language is formal and context was similar to that of the in-domain though the writing style varies. Out-domain data, however, did not give a positive impact, either in filtered or unfiltered forms, as the writing style was different and the context was much more general than that of the official government documents.