  Pretrained multilingual models are able to perform cross-lingual transfer in
a zero-shot setting, even for languages unseen during pretraining. However,
prior work evaluating performance on unseen languages has largely been limited
to low-level, syntactic tasks, and it remains unclear if zero-shot learning of
high-level, semantic tasks is possible for unseen languages. To explore this
question, 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, we
explore model adaptation via continued pretraining and provide an analysis of
the dataset by considering hypothesis-only models. We find that XLM-R's
zero-shot performance is poor for all 10 languages, with an average performance
of 38.62%. Continued pretraining offers improvements, with an average accuracy
of 44.05%. Surprisingly, training on poorly translated data by far outperforms
all other methods with an accuracy of 48.72%.
