1. Multi-Engine MT for Quick MT

2. Missing Technology for Quick MT LingWear ISI MT NICE Core Rapid MT - Multi-Engine MT - Omnivorous resource usage - Pervasive Machine Learning - Novel Approaches: * Max-Entropy models * Seeded Version Space Learning * Elicitation from native informants

3. NICE Carnegie Mellon University April 12, 2000

4. Project Members Ralf Brown: MT Jaime Carbonell: ML, MT Alon Lavie: ML, MT Lori Levin: Linguistics, MT Rodolfo Vega: International and Development Education, Information Technology in Education (IT-EDU)

5. Potential Collaborators Chile: Universidad de la Frontera Colombia: Ministry of Interior. (Ruth Connolly from OAS is looking into this.)

6. Universidad de la Frontera Instituto de Estudios Indigenas: Bilingual Multicultural Education Program Instituto de Informatica Educativa: ENLACES Project, rural component Both funded by the Ministry of Education

7. Mineduc Programs in Chile Education Quality Improvement Program, MECE ENLACES Austral Region Zonal Center: Instituto de Informatica Educativa Bilingual Multicultural Education Program La Araucania Region Projects: Instituto de Estudios Indigenas

8. Work in Year 1 Establish partnerships Collect data First version of Example-Based MT between Spanish and one indigenous language Develop elicitation corpus Build elicitation interface

9. Establishing Partnerships Identify a community that wants to work with us: design an MT application that fits in with their plans for community development or bilingual or monolingual education Identify scientists who want to work with us: linguists, computer scientists, etc. Identify non-U.S. funding sources for the indigenous community and scientists. Identify existing programs like ENLACES

10. Work in Year 2 Ongoing work from Year 1 Experiment with version space learning of translation rules Build a rule interpreter for running the translation rules