NICE: Rapid development of machine translation for low density languages

1
NICE Rapid development of machine translation
for low density languages

• New Ideas
• Use machine learning to learn translation rules
  from native speakers who are not trained in
  linguistics or computer science.
• Multi-Engine translation architecture can
  flexibly take advantage of whatever resources are
  available.
• Research partnerships with indigenous communities
  in Latin America and Alaska (Mapudungun (Chile),
  Siona (Colombia), Inupiaq (Alaska))

Interface for data elicitation

• Impact
• Rapid and low-cost development of machine
  translation for languages with scarce resources.
• Policy makers can get input from indigenous
  people.
• Indigenous people can participate in government
  and internet.

Schedule
September 01 Seeded Version Space learning
first version January 02 Example-Based Machine
Translation of Mapudungun (Chile). March 02
Multi-Engine Mapudungun system (EBMT and
partially learned transfer rules)
Carnegie Mellon University, Language Technologies
Institute L. Levin, J. Carbonell, A. Lavie, R.
Brown