IEICE TRANSACTIONS on Information
Translation of Untranslatable Words -- Integration of Lexical Approximation and Phrase-Table Extension Techniques into Statistical Machine Translation
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.12 pp.2378-2385
- Publication Date
- 2009/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.2378
- Type of Manuscript
- Special Section PAPER (Special Section on Natural Language Processing and its Applications)
- Category
- Machine Translation
Authors
Keyword
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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},}
Copy
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 -
English
