The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis. NLI benchmarks contain human ratings of entailment, but the meaning relationships driving these ratings are not formalized. Can the underlying sentence pair relationships be made more explicit in an interpretable yet robust fashion? We compare semantic structures to represent premise and hypothesis, including sets of contextualized embeddings and semantic graphs (Abstract Meaning Representations), and measure whether the hypothesis is a semantic substructure of the premise, utilizing interpretable metrics. Our evaluation on three English benchmarks finds value in both contextualized embeddings and semantic graphs; moreover, they provide complementary signals, and can be leveraged together in a hybrid model.

翻译：自然语言推理任务旨在判断给定前提（用自然语言表示）是否蕴含给定的自然语言假设。NLI基准数据集包含人类对蕴含关系的人工评分，但驱动这些评分的语义关系尚未形式化。能否以可解释且鲁棒的方式使潜在句对关系更明确？我们比较了用于表示前提和假设的语义结构，包括上下文嵌入集合与语义图（抽象意义表示），并利用可解释度量衡量假设是否为前提的语义子结构。我们在三个英文基准上的评估发现，上下文嵌入与语义图均具有价值；此外，它们提供互补信号，可在混合模型中协同利用。