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%.

探索是否可以在未见过的语言上进行高级语义任务的零-shot学习。通过AmericasNLI，测试了零-shot和翻译法等多种方法，结果发现XLM-R的零-shot表现很差，但通过持续预训练和对假设模型的考虑，有所提高。意外的是，用翻译不好的数据训练的表现最好。

AmericasNLI: 评估真正低资源语言中预训练多语种模型的零样本自然语言理解