Abstract
We propose a method that bilingually segments sentences in languages with no clear delimiter for word boundaries. In our model, we first convert the search for the segmentation into a sequential tagging problem, allowing for a polynomial-time dynamic-programming solution, and incorporate a control to balance monolingual and bilingual information at hand. Our bilingual segmentation algorithm, the integration of a monolingual language model and a statistical translation model, is devised to tokenize sentences more suitably for bilingual applications such as word alignment and machine translation. Empirical results show that bilingually-motivated segmenters outperform pure monolingual one in both the word-aligning (12% reduction in error rate) and the translating (5% improvement in BLEU) tasks, suggesting monolingual segmentation is useful in some aspects but, in a sense, not built for bilingual researches.
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Huang, CC., Chen, WT., Chang, J.S. (2008). Bilingual Segmentation for Alignment and Translation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2008. Lecture Notes in Computer Science, vol 4919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78135-6_38
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DOI: https://doi.org/10.1007/978-3-540-78135-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78134-9
Online ISBN: 978-3-540-78135-6
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