SBIR Phase I:Software-as-a-Service Customized Machine Translation for Commercial Language Service Providers and Their Clients
National Science Foundation
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Small Business Information
5124 Beeler Street, Pittsburgh, PA, 15217
Socially and Economically Disadvantaged:
AbstractThis 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.
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