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。

PropSegmEnt: 一个用于命题级别分割和蕴含识别的大规模语料库

PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition

Natural language inference (NLI) is formulated as a unified framework for
solving various NLP problems such as relation extraction, question answering,
summarization, etc. It has been studied intensively in the past few years
thanks to the availability of large-scale labeled datasets. However, most
existing studies focus on merely sentence-level inference, which limits the
scope of NLI's application in downstream NLP problems. This work presents
DocNLI -- a newly-constructed large-scale dataset for document-level NLI.
DocNLI is transformed from a broad range of NLP problems and covers multiple
genres of text. The premises always stay in the document granularity, whereas
the hypotheses vary in length from single sentences to passages with hundreds
of words. Additionally, DocNLI has pretty limited artifacts which unfortunately
widely exist in some popular sentence-level NLI datasets. Our experiments
demonstrate that, even without fine-tuning, a model pretrained on DocNLI shows
promising performance on popular sentence-level benchmarks, and generalizes
well to out-of-domain NLP tasks that rely on inference at document granularity.
Task-specific fine-tuning can bring further improvements. Data, code, and
pretrained models can be found at this https URL

介绍了 DocNLI 数据集，它是基于广泛的 NLP 问题构建的，涵盖了文本的多种类型和文档层级上的 NLI，而不是仅仅句子层级上的。实验表明，即使没有微调，预训练于 DocNLI 上的模型在流行的句子级基准测试中表现良好，并且推理到文档层级上的跨域 NLP 任务具有良好的泛化性能。