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Negation is poorly captured by current language models, although the extentof this problem is not widely understood. We introduce a natural languageinference (NLI) test suite to enable probing the capabilities of NLP methods,with the aim of understanding sub-clausal negation. The test suite containspremise--hypothesis pairs where the premise contains sub-clausal negation andthe hypothesis is constructed by making minimal modifications to the premise inorder to reflect different possible interpretations. Aside from adoptingstandard NLI labels, our test suite is systematically constructed under arigorous linguistic framework. It includes annotation of negation types andconstructions grounded in linguistic theory, as well as the operations used toconstruct hypotheses. This facilitates fine-grained analysis of modelperformance. We conduct experiments using pre-trained language models todemonstrate that our test suite is more challenging than existing benchmarksfocused on negation, and show how our annotation supports a deeperunderstanding of the current NLI capabilities in terms of negation andquantification.

Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation