@inproceedings{uchimoto-etal-2006-automatic,
title = "Automatic Detection and Semi-Automatic Revision of Non-Machine-Translatable Parts of a Sentence",
author = "Uchimoto, Kiyotaka and
Hayashida, Naoko and
Ishida, Toru and
Isahara, Hitoshi",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/496_pdf.pdf",
abstract = "We developed a method for automatically distinguishing the machine-translatable and non-machine-translatable parts of a given sentence for a particular machine translation (MT) system. They can be distinguished by calculating the similarity between a source-language sentence and its back translation for each part of the sentence. The parts with low similarities are highly likely to be non-machine-translatable parts. We showed that the parts of a sentence that are automatically distinguished as non-machine-translatable provide useful information for paraphrasing or revising the sentence in the source language to improve the quality of the translation by the MT system. We also developed a method of providing knowledge useful to effectively paraphrasing or revising the detected non-machine-translatable parts. Two types of knowledge were extracted from the EDR dictionary: one for transforming a lexical entry into an expression used in the definition and the other for conducting the reverse paraphrasing, which transforms an expression found in a definition into the lexical entry. We found that the information provided by the methods helped improve the machine translatability of the originally input sentences.",
}
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<abstract>We developed a method for automatically distinguishing the machine-translatable and non-machine-translatable parts of a given sentence for a particular machine translation (MT) system. They can be distinguished by calculating the similarity between a source-language sentence and its back translation for each part of the sentence. The parts with low similarities are highly likely to be non-machine-translatable parts. We showed that the parts of a sentence that are automatically distinguished as non-machine-translatable provide useful information for paraphrasing or revising the sentence in the source language to improve the quality of the translation by the MT system. We also developed a method of providing knowledge useful to effectively paraphrasing or revising the detected non-machine-translatable parts. Two types of knowledge were extracted from the EDR dictionary: one for transforming a lexical entry into an expression used in the definition and the other for conducting the reverse paraphrasing, which transforms an expression found in a definition into the lexical entry. We found that the information provided by the methods helped improve the machine translatability of the originally input sentences.</abstract>
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%0 Conference Proceedings
%T Automatic Detection and Semi-Automatic Revision of Non-Machine-Translatable Parts of a Sentence
%A Uchimoto, Kiyotaka
%A Hayashida, Naoko
%A Ishida, Toru
%A Isahara, Hitoshi
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F uchimoto-etal-2006-automatic
%X We developed a method for automatically distinguishing the machine-translatable and non-machine-translatable parts of a given sentence for a particular machine translation (MT) system. They can be distinguished by calculating the similarity between a source-language sentence and its back translation for each part of the sentence. The parts with low similarities are highly likely to be non-machine-translatable parts. We showed that the parts of a sentence that are automatically distinguished as non-machine-translatable provide useful information for paraphrasing or revising the sentence in the source language to improve the quality of the translation by the MT system. We also developed a method of providing knowledge useful to effectively paraphrasing or revising the detected non-machine-translatable parts. Two types of knowledge were extracted from the EDR dictionary: one for transforming a lexical entry into an expression used in the definition and the other for conducting the reverse paraphrasing, which transforms an expression found in a definition into the lexical entry. We found that the information provided by the methods helped improve the machine translatability of the originally input sentences.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/496_pdf.pdf
Markdown (Informal)
[Automatic Detection and Semi-Automatic Revision of Non-Machine-Translatable Parts of a Sentence](http://www.lrec-conf.org/proceedings/lrec2006/pdf/496_pdf.pdf) (Uchimoto et al., LREC 2006)
ACL