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Keyword: Native American languages ×Clear all

3 claims from 1 paper

1. Computer Science › Topic Modeling

   AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages

   Ebrahimi, Mager, Oncevay et al. · arXiv (Cornell University) · 2021

   The authors built AmericasNLI, a language-inference test set in 10 indigenous languages of the Americas, and tested how well XLM-R and several adaptation methods handle these unseen languages.

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   1. UncheckedXLM-R, a multilingual model, performed poorly zero-shot on all 10 indigenous American languages tested, averaging 38.62% accuracy.“We find that XLM-R's zero-shot performance is poor for all 10 languages, with an average performance of 38.62%.”
   2. UncheckedAfter continued pretraining, XLM-R averaged 44.05% accuracy on AmericasNLI's 10 indigenous languages of the Americas, up from its zero-shot result.“Continued pretraining offers improvements, with an average accuracy of 44.05%.”
   3. UncheckedTraining on poorly translated data scored 48.72% accuracy, well above every other method the authors tested on these indigenous American languages.“Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 48.72%.”