Reasoning about conjuncts in conjunctive sentences is important for a deeper
understanding of conjunctions in English and also how their usages and
semantics differ from conjunctive and disjunctive boolean logic. Existing NLI
stress tests do not consider non-boolean usages of conjunctions and use
templates for testing such model knowledge. Hence, we introduce ConjNLI, a
challenge stress-test for natural language inference over conjunctive
sentences, where the premise differs from the hypothesis by conjuncts removed,
added, or replaced. These sentences contain single and multiple instances of
coordinating conjunctions ("and", "or", "but", "nor") with quantifiers,
negations, and requiring diverse boolean and non-boolean inferences over
conjuncts. We find that large-scale pre-trained language models like RoBERTa do
not understand conjunctive semantics well and resort to shallow heuristics to
make inferences over such sentences. As some initial solutions, we first
present an iterative adversarial fine-tuning method that uses synthetically
created training data based on boolean and non-boolean heuristics. We also
propose a direct model advancement by making RoBERTa aware of predicate
semantic roles. While we observe some performance gains, ConjNLI is still
challenging for current methods, thus encouraging interesting future work for
better understanding of conjunctions. Our data and code are publicly available
at: this https URL

这篇论文介绍了一个新的自然语言推理挑战 ——ConjNLI，用于测试具有不同布尔和非布尔推理的含多个并列连词 (“and”，“or”，“but”，“nor”)、量词和否定语的结合句子的理解能力。研究表明，像 RoBERTa 这样的预训练语言模型在理解这种句子的语义时仍存在挑战，目前呼吁对于并列的更好理解和处理的未来的工作。