Context-Based Machine Translation: A Disruptive Technology for Very-High-Quality Automated Translation

Sponsor: Meaningful Machines, LLC

• Project Performance Period: 11/1/2007 - 10/31/2009
• Total project (est.): $2,927,100.00
• Requested ATP funds: $2,000,000.00

After half a century of research, the vision of a language translation machine-a computer capable of translating one natural human language into another with a facility approaching human translators-remains a distant prospect. Current machine translation (MT) systems fall into two categories: rule-based and statistical. Rule-based systems rely on an extensive set of manually-coded grammar and transfer rules between the source and target languages developed by linguists and computer scientists. Statistical MT emphasizes computer "learning" that derives translations from the analysis of large blocks of pre-translated and organized parallel texts. Meaningful Machines has proposed developing a third approach, Context-Based Machine Translation (CBMT), based on new language-processing algorithms that use a bilingual dictionary and a large body of text in the target language to build and connect target language word strings in a manner that preserves the context of the source language. CBMT, if practical, could easily be extended to many different language pairs because it obviates the need to hand-code extensive translation rules or to have access to large quantities of parallel text. Meaningful Machines proposes to generalize their existing system architecture to enable any-language to any-language translation, and improve quality for the most challenging translation tasks, such as Chinese to English. High-quality machine translation is potentially a $10 billion to $30 billion market that will benefit the nation's economy by facilitating exports, addressing critical shortages of translators and opening up new markets in areas such as daily newspapers, on-line chat services, advertising, on-line searches and automated email translation.

For project information:

Michael Steinbaum, (314) 721-1950
Mike@MeaningfulMachines.com

ATP Project Manager

Christopher Currens, (301) 975-8503
christopher.currens@nist.gov