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Context-Based Machine Translation

journal contribution
posted on 2006-01-01, 00:00 authored by Jaime G. Carbonell, Steve Klein, David Miller, Michael Steinbaum, Tomer Grassiany, Jochen Frey

Context-Based Machine TranslationTM (CBMT) is a new paradigm for corpus-based translation that requires no parallel text. Instead, CBMT relies on a lightweight translation model utilizing a full-form bilingual dictionary and a sophisticated decoder using long-range context via long n-grams and cascaded overlapping. The translation process is enhanced via in-language substitution of tokens and phrases, both for source and target, when top candidates cannot be confirmed or resolved in decoding. Substitution utilizes a synonym and near-synonym generator implemented as a corpus-based unsupervised learning process. Decoding requires a very large target-language-only corpus, and while substitution in target can be performed using that same corpus, substitution in source requires a separate (and smaller) source monolingual corpus. Spanish-to-English CBMT was tested on Spanish newswire text, achieving a BLEU score of 0.6462 in June 2006, the highest BLEU reported for any language pair. Further testing also shows that quality increases above the reported score as the target corpus size increases and as dictionary coverage of source words and phrases becomes more complete.


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