Co-occurrence Degree Based Word Alignment: A Case Study on Uyghur-Chinese

Abstract

Most widely used word alignment models are based on word co-occurrence counts in parallel corpus. However, the data sparseness during training of the word alignment model makes word co-occurrence counts of Uyghur-Chinese parallel corpus cannot indicate associations between source and target words effectively. In this paper, we propose a Uyghur-Chinese word alignment method based on word co-occurrence degree to alleviate the data sparseness problem. Our approach combine the co-occurrence counts and the fuzzy co-occurrence weights as word co-occurrence degree, fuzzy co-occurrence weights can be obtained by searching for fuzzy co-occurrence word pairs and computing differences of length between current Uyghur word and other Uyghur words in fuzzy co-occurrence word pairs. Experiment shows that with the co-occurrence degree based word alignment model, the performance of Uyghur-Chinese word alignment result is outperform the baseline word alignment model, the quality of Uyghur-Chinese machine translation also improved.