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Advancing Machine Translation for Scarce Resource Languages: The AVENUE Project

The AVENUE Project addresses the challenges of machine translation for languages with scarce resources, like Mapudungun from Chile. With limited electronic text and few linguists proficient in computational rules, our approach involves learning translation rules directly from bilingual informants. Utilizing machine learning, we aim for a low-cost, rapid development of translation systems. Our partnerships with indigenous communities in Latin America and Alaska ensure their languages are represented in technology, allowing native speakers to contribute to their language's digitalization and preservation.

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Advancing Machine Translation for Scarce Resource Languages: The AVENUE Project

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  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)) 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 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) Carnegie Mellon University, Language Technologies Institute: L. Levin, J. Carbonell, A. Lavie, R. Brown

  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

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