论文标题
conjnli：关于连词句子的自然语言推断
ConjNLI: Natural Language Inference Over Conjunctive Sentences
论文作者
论文摘要
关于连词句子中有关连词的推理对于更深入地理解英语连词以及它们的用法和语义与结合性和脱节的布尔逻辑有何不同。现有的NLI应力测试不考虑连词的非树状用法，并使用模板来测试这种模型知识。因此，我们介绍了Conjnli，这是对结合句子的自然语言推论的挑战应力测试，在该句子中，前提与删除，添加或更换的结合物的假设不同。这些句子包含与量词，否定符，需要多种布尔和非凉型推论的单一和多个实例（“和”，“或”，“，”，“ Nor”）的单一和多个实例。我们发现，像罗伯塔这样的大规模训练的大规模训练的语言模型并不理解连词语义，并诉诸浅启发式方法，以对这种句子进行推断。作为某些初始解决方案，我们首先提出了一种基于布尔和非树脂启发式方法的合成训练数据的迭代对抗微调方法。我们还通过使罗伯塔（Roberta）意识到谓语语义角色来提出直接模型的发展。尽管我们观察到一些性能提高，但Conjnli仍然对当前方法充满挑战，从而鼓励未来的工作以更好地理解连词。我们的数据和代码可公开可用：https：//github.com/swarnahub/conjnli
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: https://github.com/swarnaHub/ConjNLI
