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Compared to English, German word order is freer and therefore posesadditional challenges for natural language inference (NLI). We create WOGLI(Word Order in German Language Inference), the first adversarial NLI datasetfor German word order that has the following properties: (i) each premise hasan entailed and a non-entailed hypothesis; (ii) premise and hypotheses differonly in word order and necessary morphological changes to mark case and number.In particular, each premise andits two hypotheses contain exactly the samelemmata. Our adversarial examples require the model to use morphologicalmarkers in order to recognise or reject entailment. We show that current Germanautoencoding models fine-tuned on translated NLI data can struggle on thischallenge set, reflecting the fact that translated NLI datasets will not mirrorall necessary language phenomena in the target language. We also examineperformance after data augmentation as well as on related word order phenomenaderived from WOGLI. Our datasets are publically available athttps://github.com/ireinig/wogli.

Can current NLI systems handle German word order? Investigating language model performance on a new German challenge set of minimal pairs