TY - JOUR
T1 - Purest ever example-based machine translation
T2 - Detailed presentation and assessment
AU - Lepage, Yves
AU - Denoual, Etienne
N1 - Funding Information:
Acknowledgements The research reported here was supported in part by a contract with the Japanese National Institute of Information and Communications Technology entitled “A study of speech dialogue translation technology based on a large corpus”. Both authors were at the time of writing with the Japanese National Institute of Information and Communications Technology (NiCT). We are particularly indebted to Prof. C. Boitet for his many comments on an earlier version of the draft that considerably helped to improve clarity. Thanks also to the reviewers who pointed out some errors in the draft.
PY - 2005/12
Y1 - 2005/12
N2 - We have designed, implemented and assessed an EBMT system that can be dubbed the "purest ever built": it strictly does not make any use of variables, templates or patterns, does not have any explicit transfer component, and does not require any preprocessing or training of the aligned examples. It uses only a specific operation, proportional analogy, that implicitly neutralizes divergences between languages and captures lexical and syntactic variations along the paradigmatic and syntagmatic axes without explicitly decomposing sentences into fragments. Exactly the same genuine implementation of such a core engine was evaluated on different tasks and language pairs. To begin with, we compared our system on two tasks of a previous MT evaluation campaign to rank it among other current state-of-the-art systems. Then, we illustrated the "universality" of our system by participating in a recent MT evaluation campaign, with exactly the same core engine, for a wide variety of language pairs. Finally, we studied the influence of extra data like dictionaries and paraphrases on the system performance.
AB - We have designed, implemented and assessed an EBMT system that can be dubbed the "purest ever built": it strictly does not make any use of variables, templates or patterns, does not have any explicit transfer component, and does not require any preprocessing or training of the aligned examples. It uses only a specific operation, proportional analogy, that implicitly neutralizes divergences between languages and captures lexical and syntactic variations along the paradigmatic and syntagmatic axes without explicitly decomposing sentences into fragments. Exactly the same genuine implementation of such a core engine was evaluated on different tasks and language pairs. To begin with, we compared our system on two tasks of a previous MT evaluation campaign to rank it among other current state-of-the-art systems. Then, we illustrated the "universality" of our system by participating in a recent MT evaluation campaign, with exactly the same core engine, for a wide variety of language pairs. Finally, we studied the influence of extra data like dictionaries and paraphrases on the system performance.
KW - Divergences across languages
KW - Example-based machine translation
KW - Proportional analogies
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U2 - 10.1007/s10590-006-9010-x
DO - 10.1007/s10590-006-9010-x
M3 - Article
AN - SCOPUS:33847301037
SN - 0922-6567
VL - 19
SP - 251
EP - 282
JO - Machine Translation
JF - Machine Translation
IS - 3-4
ER -