Sentence Meaning and Natural Language Inference › Summary

Sentence meaning is compositional and truth-conditional. It depends on word meanings, syntactic structure, scope, and context. NLI formalizes three core relations: entailment, contradiction, and neutrality. Entailment is directional. Contradiction is not the same as falsehood. Neutral is not ignorance; it means the premise does not determine the hypothesis.

Presupposition and conversational implicature differ from ordinary asserted entailment. Presuppositions can project under negation, depending on context; conversational implicatures are typically cancellable. A three-way NLI label does not separately identify these meaning layers.

Transformer cross-encoders predict labels from paired text, with datasets such as SNLI, MultiNLI, and ANLI supplying supervision and evaluation examples. They can still learn shortcuts. Evaluate those risks with diagnostic cases, hypothesis-only baselines, category-level results, and checks appropriate to the data and model. For prompted LLMs, record the prompt and decoding protocol and investigate benchmark overlap.

The code battery in this chapter runs a fixed lexical baseline on controlled examples, tests directional entailment, probes presupposition, and computes a confusion matrix. Its hand-selected weights and normalized scores are illustrative; they are not measurements of a pretrained model. The results are illustrative, not definitive. The main takeaway is that NLI is a way to study sentence meaning, not a replacement for semantic theory. In the next chapter, we move from sentence-level entailment to the internal structure of predicates and their arguments: semantic roles and argument structure.