Linguistic tuple segmentation in ngram-ba

Ngram-based Statistical Machine Translation relies on a standard Ngram language model of tuples to estimate the translation process. In training, this translation model requires a segmentation of each parallel sentence, which involves taking a hard decision on tuple segmentation when a word is not linked during word alignment. This is especially critical when this word appears in the target language, as this hard decision is compulsory. In this paper we present a thorough study of this situation, comparing for the first time each of the proposed techniques in two independent tasks, namely English–Spanish European Parliament Proceedings large-vocabulary task and Arabic–English Basic Travel Expressions small-data task. In the face of this comparison, we present a novel segmentation technique which incorporates linguistic information. Results obtained in both tasks outperform all previous techniques.