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Automatic induction of shallow-transfer rules for open-source machine translation (2007)
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| Venue: | In Proceedings of the 11th Conference on Theoretical and Methodological Issues in Machine Translation (TMI 2007 |
| Citations: | 4 - 3 self |
BibTeX
@INPROCEEDINGS{Forcada07automaticinduction,
author = {Mikel L. Forcada},
title = {Automatic induction of shallow-transfer rules for open-source machine translation},
booktitle = {In Proceedings of the 11th Conference on Theoretical and Methodological Issues in Machine Translation (TMI 2007},
year = {2007},
pages = {181}
}
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Abstract
This paper focuses on the infer-ence of structural transfer rules for shallow-transfer machine translation (MT). Transfer rules are generated from alignment templates, like those used in statistical MT, that have been extracted from parallel cor-pora and extended with a set of re-strictions that control their applica-tion. The experiments conducted using the open-source MT platform Apertium show that there is a clear improvement in translation quality as compared to word-for-word trans-lation (when no transfer rules are used), and that the resulting transla-tion quality is very close to the one obtained using hand-coded transfer rules. The method we present is entirely unsupervised and benefits from information in the rest of mod-ules of the MT system in which the inferred rules are applied. 1
Keyphrases
automatic induction open-source machine translation shallow-transfer rule transfer rule shallow-transfer machine translation parallel cor-pora structural transfer rule word-for-word trans-lation hand-coded transfer rule alignment template mt system inferred rule translation quality clear improvement transla-tion quality statistical mt open-source mt platform apertium show
