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Reasoning about conjuncts in conjunctive sentences is important for a deeperunderstanding of conjunctions in English and also how their usages andsemantics differ from conjunctive and disjunctive boolean logic. Existing NLIstress tests do not consider non-boolean usages of conjunctions and usetemplates for testing such model knowledge. Hence, we introduce ConjNLI, achallenge stress-test for natural language inference over conjunctivesentences, where the premise differs from the hypothesis by conjuncts removed,added, or replaced. These sentences contain single and multiple instances ofcoordinating conjunctions ("and", "or", "but", "nor") with quantifiers,negations, and requiring diverse boolean and non-boolean inferences overconjuncts. We find that large-scale pre-trained language models like RoBERTa donot understand conjunctive semantics well and resort to shallow heuristics tomake inferences over such sentences. As some initial solutions, we firstpresent an iterative adversarial fine-tuning method that uses syntheticallycreated training data based on boolean and non-boolean heuristics. We alsopropose a direct model advancement by making RoBERTa aware of predicatesemantic roles. While we observe some performance gains, ConjNLI is stillchallenging for current methods, thus encouraging interesting future work forbetter understanding of conjunctions. Our data and code are publicly availableat: https://github.com/swarnaHub/ConjNLI

ConjNLI: Natural Language Inference Over Conjunctive Sentences