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In this paper, we present InferES - an original corpus for Natural LanguageInference (NLI) in European Spanish. We propose, implement, and analyze avariety of corpus-creating strategies utilizing expert linguists and crowdworkers. The objectives behind InferES are to provide high-quality data, and,at the same time to facilitate the systematic evaluation of automated systems.Specifically, we focus on measuring and improving the performance of machinelearning systems on negation-based adversarial examples and their ability togeneralize across out-of-distribution topics.  We train two transformer models on InferES (8,055 gold examples) in a varietyof scenarios. Our best model obtains 72.8% accuracy, leaving a lot of room forimprovement. The "hypothesis-only" baseline performs only 2%-5% higher thanmajority, indicating much fewer annotation artifacts than prior work. We findthat models trained on InferES generalize very well across topics (both in- andout-of-distribution) and perform moderately well on negation-based adversarialexamples.

InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples