  XNLI is a popular Natural Language Inference (NLI) benchmark widely used to
evaluate cross-lingual Natural Language Understanding (NLU) capabilities across
languages. In this paper, we expand XNLI to include Basque, a low-resource
language that can greatly benefit from transfer-learning approaches. The new
dataset, dubbed XNLIeu, has been developed by first machine-translating the
English XNLI corpus into Basque, followed by a manual post-edition step. We
have conducted a series of experiments using mono- and multilingual LLMs to
assess a) the effect of professional post-edition on the MT system; b) the best
cross-lingual strategy for NLI in Basque; and c) whether the choice of the best
cross-lingual strategy is influenced by the fact that the dataset is built by
translation. The results show that post-edition is necessary and that the
translate-train cross-lingual strategy obtains better results overall, although
the gain is lower when tested in a dataset that has been built natively from
scratch. Our code and datasets are publicly available under open licenses.
