Integration of Multiple Bilingually-Learned Segmentation Schemes into Statistical Machine Translation

This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches. The method can be applied to any language pair where the source language is unsegmented and the target language segmentation is known. First, an iterative bootstrap method is applied to learn multiple segmentation schemes that are consistent with the phrasal segmentations of an SMT system trained on the resegmented bitext. In the second step, multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating five Asian languages into English revealed that the method of integrating multiple segmentation schemes outperforms SMT models trained on any of the learned word segmentations and performs comparably to available state-ofthe-art monolingually-built segmentation tools.