The widely studied task of Natural Language Inference (NLI) requires a system
to recognize whether one piece of text is textually entailed by another, i.e.
whether the entirety of its meaning can be inferred from the other. In current
NLI datasets and models, textual entailment relations are typically defined on
the sentence- or paragraph-level. However, even a simple sentence often
contains multiple propositions, i.e. distinct units of meaning conveyed by the
sentence. As these propositions can carry different truth values in the context
of a given premise, we argue for the need to recognize the textual entailment
relation of each proposition in a sentence individually.
We propose PropSegmEnt, a corpus of over 35K propositions annotated by expert
human raters. Our dataset structure resembles the tasks of (1) segmenting
sentences within a document to the set of propositions, and (2) classifying the
entailment relation of each proposition with respect to a different yet
topically-aligned document, i.e. documents describing the same event or entity.
We establish strong baselines for the segmentation and entailment tasks.
Through case studies on summary hallucination detection and document-level NLI,
we demonstrate that our conceptual framework is potentially useful for
understanding and explaining the compositionality of NLI labels.

本论文提出了 PropSegmEnt，这是一个超过 35K 的已由人类专家标注的命题语料库，用于 NLI 任务中的命题分割和关系分类，证明了这个框架有助于理解和解释 NLI 标签的组合性。