Noisy channel models, widely used in modern spellers, cope with typical misspellings, but do not work well with infrequent and difficult spelling errors. In this paper, we have improved the noisy channel approach by iterative stochastic search for the best correction. The proposed algorithm allowed us to avoid local minima problem and improve the F 1 measure by 6.6% on distant spelling errors.
We describe the English-Turkish and Turkish-English translation systems submitted by Yandex School of Data Analysis team to WMT16 news translation task. We successfully applied hand-crafted morphological (de-)segmentation of Turkish, syntax-based pre-ordering of English in English-Turkish and post-ordering of English in Turkish-English. We perform desegmentation using SMT and propose a simple yet efficient modification of postordering. We also show that Turkish morphology and word order can be handled in a fully-automatic manner with only a small loss of BLEU.
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