SBIR Phase II: NUMBERS: Bringing Statistical Machine Translation into the Real World

The goal of this Small Business Innovation Research (SBIR) Phase II project is to bring radically new technology to the machine translation marketplace. While current systems are rule-based and difficult to extend, this company employs a statistical system that learns to translate by automatically analyzing large collections of previously translated material. This technology already outperforms rule-based systems, and it easily adapts to specific domains of interest, such as technical documentation generated by multinational corporations. The company has licensed (and co-developed) key software engines from the founders' research team at USC/ISI, a world leader in machine translation. In this project, they will extend their statistical engine in three ways, driven by customer needs -- they propose to build (1) a parallel, cluster-based training system for handling large text volumes (2) new capabilities for translating numbers, dates, personal names, locations, etc. ("named entities"), and (3) rapid customization tools that will assist with customized translation engines for specific customer domain requirements. There has been substantial client/funding interest from intelligence agencies, corporate users, and angel and venture investment groups. Over the next year, the plan is to capitalize on this interest by developing several clients and shipping the first products.