Abstract
Statistical Machine Translation is receiving more and more attention every day due to the success that the phrase-based alignment models are obtaining. However, despite their power, state-of-the-art systems using these models present a series of disadvantages that lessen their effectiveness in working environments where temporal or spacial computational resources are limited. A finite-state framework represents an interesting alternative because it constitutes an efficient paradigm where quality and realtime factors are properly integrated in order to build translation devices that may be of help for their potential users. Here, we describe a way to use the bilingual information in a phrase-based model in order to implement a phrase-based ngram model using finite state transducers. It will be worth the trouble due to the notable decrease in computational requirements that finite state transducers present in practice with respect to the use of some well-known stack-decoding algorithms. Results for the French-English EuroParl benchmark corpus from the 2006 Workshop on Machine Translation of the ACL are reported.
This work has been partially supported by the EC (FEDER) and the Spanish projects TIN2006-15694-C02-01 and the Consolider Ingenio 2010 CSD2007-00018.
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González, J., Sanchis, G., Casacuberta, F. (2008). Learning Finite State Transducers Using Bilingual Phrases. 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_35
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