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.Comment: International Conference on Computational Semantics (IWCS 2023); v2
fixes an imprecise sentence below Eq.