1. Machine Translation with Scarce Resources The Avenue Project

2. Scarce Resources Not much text in electronic form. Very few linguists who can write computational rules. No standard orthography –Kudaw, kusaw (work) (Mapudungun, Chile) –Not even sure of pronunciation: EH-nvelope, AH-nvelope (envelope) (English, US, not a language with scarce resources)

3. Our Approach Learn rules from a controlled corpus. Corpus is elicited from bilingual speakers. The informant only needs to translate and align words.

4. AVENUE Project 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)) Carnegie Mellon University, Language Technologies Institute: L. Levin, J. Carbonell, A. Lavie, R. Brown 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 Year 1: Seeded Version Space learning– first version Year 2: Example-Based Machine Translation of Mapudungun (Chile). Year 3: Multi-Engine Mapudungun system (EBMT and partially learned transfer rules) Interface for data elicitation

5. Elicitation Interface

6. Elicitation Corpus: example English : I fell. Spanish: Caí Mapudungun: Tranün English: I am falling. Spanish: Estoy cayendo Mapudungun: Tranmeken

7. Elicitation Corpus: example English: You (John) fell. Spanish: Tu (Juan) caiste Mapudungun: Eymi tranimi (Kuan) English: You (Mary) fell. Spanish: Tu (María) caiste Mapudungun: Eymi tranimi (Maria) English: The rock fell. Spanish: La piedra cayó Mapudungun: Trani chi kura