Automated generalization of translation examples
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Extracting paraphrases from a parallel corpus
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Improved statistical machine translation using paraphrases
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Improved statistical machine translation using monolingually-derived paraphrases
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
The CMU-EBMT machine translation system
Machine Translation
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Out-of-vocabulary (OOV) words present a significant challenge for Machine Translation. For low-resource languages, limited training data increases the frequency of OOV words and this degrades the quality of the translations. Past approaches have suggested using stems or synonyms for OOV words. Unlike the previous methods, we show how to handle not just the OOV words but rare words as well in an Example-based Machine Translation (EBMT) paradigm. Presence of OOV words and rare words in the input sentence prevents the system from finding longer phrasal matches and produces low quality translations due to less reliable language model estimates. The proposed method requires only a monolingual corpus of the source language to find candidate replacements. A new framework is introduced to score and rank the replacements by efficiently combining features extracted for the candidate replacements. A lattice representation scheme allows the decoder to select from a beam of possible replacement candidates. The new framework gives statistically significant improvements in English-Chinese and English-Haitian translation systems.