The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo propõe um método para lidar com palavras fora do vocabulário (OOV) que não podem ser traduzidas usando sistemas convencionais de tradução automática estatística (SMT) baseados em frases. Para uma determinada palavra OOV, técnicas de aproximação lexical são utilizadas para identificar variantes ortográficas e flexionais de palavras que ocorrem nos dados de treinamento. Todas as palavras OOV na frase fonte são então substituídas por variantes de palavras apropriadas encontradas no corpus de treinamento, reduzindo assim o número de palavras OOV na entrada. Além disso, a fim de aumentar a cobertura de tais traduções de palavras, o modelo de tradução SMT é estendido adicionando novas traduções de frases para todas as palavras do idioma de origem que não possuem uma entrada de palavra única na tabela de frases original, mas apenas aparecem no contexto de frases maiores. A eficácia dos métodos propostos é investigada para a tradução de hindi para inglês, chinês e japonês.
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Michael PAUL, Karunesh ARORA, Eiichiro SUMITA, "Translation of Untranslatable Words -- Integration of Lexical Approximation and Phrase-Table Extension Techniques into Statistical Machine Translation" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 12, pp. 2378-2385, December 2009, doi: 10.1587/transinf.E92.D.2378.
Abstract: This paper proposes a method for handling out-of-vocabulary (OOV) words that cannot be translated using conventional phrase-based statistical machine translation (SMT) systems. For a given OOV word, lexical approximation techniques are utilized to identify spelling and inflectional word variants that occur in the training data. All OOV words in the source sentence are then replaced with appropriate word variants found in the training corpus, thus reducing the number of OOV words in the input. Moreover, in order to increase the coverage of such word translations, the SMT translation model is extended by adding new phrase translations for all source language words that do not have a single-word entry in the original phrase-table but only appear in the context of larger phrases. The effectiveness of the proposed methods is investigated for the translation of Hindi to English, Chinese, and Japanese.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2378/_p
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@ARTICLE{e92-d_12_2378,
author={Michael PAUL, Karunesh ARORA, Eiichiro SUMITA, },
journal={IEICE TRANSACTIONS on Information},
title={Translation of Untranslatable Words -- Integration of Lexical Approximation and Phrase-Table Extension Techniques into Statistical Machine Translation},
year={2009},
volume={E92-D},
number={12},
pages={2378-2385},
abstract={This paper proposes a method for handling out-of-vocabulary (OOV) words that cannot be translated using conventional phrase-based statistical machine translation (SMT) systems. For a given OOV word, lexical approximation techniques are utilized to identify spelling and inflectional word variants that occur in the training data. All OOV words in the source sentence are then replaced with appropriate word variants found in the training corpus, thus reducing the number of OOV words in the input. Moreover, in order to increase the coverage of such word translations, the SMT translation model is extended by adding new phrase translations for all source language words that do not have a single-word entry in the original phrase-table but only appear in the context of larger phrases. The effectiveness of the proposed methods is investigated for the translation of Hindi to English, Chinese, and Japanese.},
keywords={},
doi={10.1587/transinf.E92.D.2378},
ISSN={1745-1361},
month={December},}
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TY - JOUR
TI - Translation of Untranslatable Words -- Integration of Lexical Approximation and Phrase-Table Extension Techniques into Statistical Machine Translation
T2 - IEICE TRANSACTIONS on Information
SP - 2378
EP - 2385
AU - Michael PAUL
AU - Karunesh ARORA
AU - Eiichiro SUMITA
PY - 2009
DO - 10.1587/transinf.E92.D.2378
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2009
AB - This paper proposes a method for handling out-of-vocabulary (OOV) words that cannot be translated using conventional phrase-based statistical machine translation (SMT) systems. For a given OOV word, lexical approximation techniques are utilized to identify spelling and inflectional word variants that occur in the training data. All OOV words in the source sentence are then replaced with appropriate word variants found in the training corpus, thus reducing the number of OOV words in the input. Moreover, in order to increase the coverage of such word translations, the SMT translation model is extended by adding new phrase translations for all source language words that do not have a single-word entry in the original phrase-table but only appear in the context of larger phrases. The effectiveness of the proposed methods is investigated for the translation of Hindi to English, Chinese, and Japanese.
ER -