October 2005

Translation by Numbers

Language Weaver's machine-translation software aids homeland security.

By Gregory T. Huang

Bryce Benjamin knew a winner when he saw one. It was December 2001, and the infotech entrepreneur was meeting with two professors who were starting a company to commercialize "statistical machine translation." Their breakthrough: software that could learn automatically to translate text from one language into another.

Benjamin believed the technology was desperately needed for purposes of both homeland security and business communication. The company's location -- on the water in sunny Marina del Rey, CA -- didn't hurt either. "I looked out at the view," he says, "and I thought, 'This deal has a lot of promise.'"

Today the trio's 35-person startup, Language Weaver, is one of the leading companies in the burgeoning field of machine translation. For U.S. counterterror translators facing a growing backlog of untranslated audiotapes and communiques, software is increasingly the weapon of choice. Multinational corporations like Google, Yahoo, and Microsoft -- not to mention smaller companies with global staff -- are also driving the demand for machine translation of technical documents and Web pages.

Language Weaver's software translates text between English and half a dozen other languages, including Arabic, Chinese, and Spanish. So far, the technology is most useful as a screening tool that monitors reams of foreign-language news broadcasts, chat rooms, and websites. "People use our translation software to determine the relevance of information, as a triage function," says Benjamin, the company's CEO. "It's very good at telling what a certain passage is about."

Most machine-translation systems work on individual words or use complicated sets of translation guidelines, which must be devised by linguists and coded by hand. Language Weaver's technology, which company cofounders Kevin Knight and Daniel Marcu developed at the University of Southern California's Information Sciences Institute (ISI), takes a different tack. It uses human translation data, such as United Nations transcripts, to set up "parallel corpora" of text passages in two languages, aligned sentence by sentence.