Dealing with Out-Of-Vocabulary Problem in Sentence Alignment Using Word Similarity

Sentence alignment plays an essential role in building bilingual corpora which are valuable resources for many applications like statistical machine translation. In various approaches of sentence alignment, length-and-word-based methods which are based on sentence length and word correspondences have been shown to be the most effective. Nevertheless a drawback of using bilingual dictionaries trained by IBM Models in length-and-word-based methods is the problem of out-of-vocabulary (OOV). We propose using word similarity learned from monolingual corpora to overcome the problem. Experimental results showed that our method can reduce the OOV ratio and achieve a better performance than some other lengthand-word-based methods. This implies that using word similarity learned from monolingual data may help to deal with OOV problem in sentence alignment.