Experiments with Russian to Kazakh sentence alignment

Abstract

Sentence alignment is the final step in building parallel corpora, which arguably has the greatest impact on the quality of a resulting corpus and the accuracy of machine translation systems that use it for training. However, the quality of sentence alignment itself depends on a number of factors. In this paper we investigate the impact of several data processing techniques on the quality of sentence alignment. We develop and use a number of automatic evaluation metrics, and provide empirical evidence that application of all of the considered data processing techniques yields bitexts with the lowest ratio of noise and the highest ratio of parallel sentences.