The LETRAS project investigates novel approaches to development of Machine Translation (MT) technology, with the goal of establishing a general framework that supports building MT prototype systems for languages for which only limited amounts of data and resources in electronic form are available. The research focus of the project is on automatic learning of translation transfer-rules from limited amounts of elicited bilingual data. A new run-time translation "engine" maps source language sentences to their target language equivalents, by building a large structure of possible partial translations and then applying effective search techniques for recovering the best translation. In the last stage, an automatic rule refinement module helps the system learn how to correct and improve its imperfect translation rules, based on feedback on translation errors provided by users. MT prototype systems for several language pairs are being constructed as an integral part of the project and in collaboration with external research groups. The prototypes guide our research and test out our new ideas. At the same time, our collaborations with local researchers and native communities promote the development of information technology for native languages and educate local researchers with our state-of-the-art MT research. The prototypes include a Hebrew-to-English MT system (with University of Haifa, Israel); an Inupiaq-to-English MT system (with University of Alaska, Fairbanks, and the Inupiat Heritage Center in Barrow, Alaska); and a Karitiana-to-Portuguese MT system (with University of Sao Paulo, Brazil). Support for the Alaska collaboration is being provided by NSF's Office of Polar Programs (OPP), and support for the collaborations with Israel and Brazil is being provided by NSF's Office for International Science and Engineering (OISE). OISE is also providing funding for a planning trip to Bolivia to explore a possible Aymara-to-Spanish project. The potential long-term impact of the project is profound - enabling the development of Machine Translation for many languages of the world, which in turn opens the door for active participation of native and minority communities in the information-rich activities of the 21st century.
