A Data Selection Method for Domain-specific Machine Translation

Neural machine translation performs well in general domains with large-scale bilingual corpora, but it is not very effective in specific domains with only a small amount of bilingual corpora. Data selection technology is one of the main methods to alleviate the problem of bilingual data scarcity in specific domains by selecting corpus that is close to the domain corpus from large-scale general domain corpora for data expansion. Due to the large scale of general data, traditional dimension reduction methods are inefficient when dealing with massive data. This paper proposes a two-stage data selection strategy, which improves the efficiency of data selection and the data quality at the same time. First, the Inverted File System Product Quantization (IVFPQ) method is used to preliminarily select data, which improves the computational efficiency and the quality of data selection. In order to obtain data with higher quality, on this basis, this paper trains a domain classifier to fully consider the domain characteristics, and further selects higher quality data. Experimental results show that compared with the baseline method, a better domain-specific machine translation model can be trained using the expanded data obtained by this method.