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Pretrained multilingual models are able to perform cross-lingual transfer ina zero-shot setting, even for languages unseen during pretraining. However,prior work evaluating performance on unseen languages has largely been limitedto low-level, syntactic tasks, and it remains unclear if zero-shot learning ofhigh-level, semantic tasks is possible for unseen languages. To explore thisquestion, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018)to 10 indigenous languages of the Americas. We conduct experiments with XLM-R,testing multiple zero-shot and translation-based approaches. Additionally, weexplore model adaptation via continued pretraining and provide an analysis ofthe dataset by considering hypothesis-only models. We find that XLM-R'szero-shot performance is poor for all 10 languages, with an average performanceof 38.62%. Continued pretraining offers improvements, with an average accuracyof 44.05%. Surprisingly, training on poorly translated data by far outperformsall other methods with an accuracy of 48.72%.

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