When building rule-based machine translation systems, a considerable human effort is needed to code the transfer rules that are able to translate source-language sentences into grammatically correct target-language sentences. In this paper we describe how to adapt the alignment templates used in statistical machine translation to the rule-based machine translation framework. The alignment templates are converted into structural transfer rules that are used by a shallow-transfer machine translation engine to produce grammatically correct translations. As the experimental results show there is a considerable improvement in the translation quality as compared to word-for-word translation (when no transfer rules are used), and the translation quality is close to that achieved when hand-coded transfer rules are used. The method presented is entirely unsupervised, and needs only a parallel corpus, two morphological analysers, and two part-of-speech taggers, such as those used by the machine translation system in which the inferred transfer rules are integrated. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Sánchez-Martínez, F., & Ney, H. (2006). Using alignment templates to infer shallow-transfer machine translation rules. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4139 LNAI, pp. 756–767). Springer Verlag. https://doi.org/10.1007/11816508_75
Mendeley helps you to discover research relevant for your work.