LREC 2006 - Proceedings sorted by papers

Title	Automatic Detection and Semi-Automatic Revision of Non-Machine-Translatable Parts of a Sentence
Authors	K. Uchimoto, N. Hayashida, T. Ishida, H. Isahara
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.
Keywords	machine translation, translation aid, machine translatability, similarity, back translation, non-machine-translatable parts, knowledge extraction, paraphrase
Full paper	Automatic Detection and Semi-Automatic Revision of Non-Machine-Translatable Parts of a Sentence

Title

Automatic Detection and Semi-Automatic Revision of Non-Machine-Translatable Parts of a Sentence

Authors

K. Uchimoto, N. Hayashida, T. Ishida, H. Isahara

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.

Keywords

machine translation, translation aid, machine translatability, similarity, back translation, non-machine-translatable parts, knowledge extraction, paraphrase

Full paper

Automatic Detection and Semi-Automatic Revision of Non-Machine-Translatable Parts of a Sentence