Data Selection With Fewer Words

We present a method that improves data selection by combining a hybrid word/part-of-speech representation for corpora, with the idea of distinguishing between rare and frequent events. We validate our approach using data selection for machine translation, and show that it maintains or improves BLEU and TER translation scores while substantially improving vocabulary coverage and reducing data selection model size. Paradoxically, the coverage improvement is achieved by abstracting away over 97% of the total training corpus vocabulary using simple part-of-speech tags during the data selection process.