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Multi-Engine MT for Quick MT

This project focuses on leveraging multi-engine machine translation (MT) to improve educational resources in bilingual and indigenous communities. By using novel approaches such as Max-Entropy models and elicitation from native informants, the project aims to develop an MT application that aligns with community development plans. Collaboration with universities and educational institutions in Chile and Colombia fosters data collection and the establishment of partnerships. The initiative is rooted in enhancing the quality of education and promoting bilingual multicultural education for indigenous languages.

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Multi-Engine MT for Quick MT

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  1. Multi-Engine MT for Quick MT

  2. Missing Technology for Quick MT NICE LingWear 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 ISI MT

  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

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