SBIR-STTR Award

This Small Business Innovation Research (SBIR) Phase I project implements and tests the efficacy of a software approach for utilizing client-specific data in order to customize fully-automatic translations produced by state-of-the-art Machine Translation (MT) systems. Based on this approach, the company leverages databases of previously-translated material in order to produce client-customized high-quality fully-automatic translations for commercial language service providers (LSPs) and their enterprise clients. These services are provided via a "software-as-a-service" (SaaS) model. The proposed approach provides a dramatically less-expensive solution for creating client-specific customized MT engines. While it is broadly recognized that customization to client- and domain-specific data can greatly boost the translation quality of MT systems, the common approach of customizing the MT engine directly is costly, and is consequently practical only for major commercial enterprises with very large translation volumes. The company uses the client-specific data maintained by LSPs for their enterprise clients in order to augment and modify the translations produced by a state-of-the-art generic MT system. The same client-customized MT systems can also be incorporated into the human-based workflow used by LSPs for producing human-quality translations for clients, reducing human translator effort concomitantly with the overall cost and duration of translation projects. The SaaS-based services proposed solutions have the potential of fundamentally changing the commercial translation landscape by removing barriers to wide-spread adoption of MT technology by the broad LSP industry and their even broader client-base. The industry is dominated by a large number of small and medium size LSPs which possess large volumes of client-specific translation data, but lack the resources and the know-how to develop MT-based solutions that leverage these data. By partnering with such LSPs, the company can quickly gain access to a large number of commercial enterprise clients through the LSPs' existing business relationships

This Small Business Innovation Research Phase-II project develops advanced technology capabilities for constructing and deploying client-adapted automated language translation systems within commercial settings that are used by globalizing enterprises and the language service provider companies (LSPs) that provide translation services to such enterprise clients. The developed technology leverages databases of previously-translated material in order to produce client-adapted high-quality fully-automatic translations for commercial language service providers (LSPs) and their enterprise clients. This approach provides a scalable and less-costly solution for creating and deploying client-specific customized Machine Translation (MT) engines. Once deployed, these customized MT systems expand the capabilities of clients to translate volumes of content that are not feasible to translate using current methods. The broader impact/commercial potential of this project lies in the impact that it will have on the broad commercial translation industry. The technology developed in the project is likely to significantly reduce barriers to wide-spread adoption of MT technology by the broad LSP industry and their enterprise client-base. The 2010 commercial translation market is a $26 billion industry, growing at a healthy pace. Current commercial MT offerings are expensive and too difficult to deploy for most enterprises and service providers. Free web-based translation services serve casual users, but do not meet the quality and security needs of enterprises. The technology developed in this project and the cloud-based delivery model support scalable, easy-to-integrate MT services, which are highly attractive to a broad range of potential clients. This approach will support cost-effective content generation into multiple target languages at a massive scale, a capability that is essential for globalizing US enterprises in order to compete in the information-rich market place of the 21st century.