Learning Translation Templates from Bilingual Translation Examples
Applied Intelligence
A systematic comparison of various statistical alignment models
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
An efficient method for determining bilingual word classes
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Learning translation templates from bilingual text
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The Alignment Template Approach to Statistical Machine Translation
Computational Linguistics
Open-Source portuguese–spanish machine translation
PROPOR'06 Proceedings of the 7th international conference on Computational Processing of the Portuguese Language
Inferring shallow-transfer machine translation rules from small parallel corpora
Journal of Artificial Intelligence Research
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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.