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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 45K 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.

The more data you can feed a machine learning algorithm, the better it can spot patterns, make decisions, predict behaviours, personalise content, diagnose medical conditions, power smart everything, detect cyber threats and fraud; indeed, AI and data make for a happy partnership: "The algorithm without data is blind. Data without algorithms is dumb." Not everyone wants to share, at least, not under the current rules of digital engagement. Some individuals disengage entirely, becoming digital hermits. Others proceed with caution, using privacy-enhancing technologies (PETs) to plug the digital leak: a kind karate chop, digital self defense -- they don't trust website privacy notices, they verify them with tools like DuckDuckGo's Privacy Grade extension and soon, machine-readable privacy notices.

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