AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
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Abstract (arXiv)
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%.
Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). Builds on 37 of its 96 references that are in the map. First posted on arXiv 18 Apr 2021.
Builds on
37 of 96 references.
References · 37
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- 2023Improving Language Plasticity via Pretraining with Active Forgettingcited by 43
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- 2023When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languagescited by 32
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