Methoden vergleichen

	Textual Entailment	Zero-Shot-Klassifikation – Textklassifikation ohne Trainingsdaten
Fachgebiet	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	—	2019
Urheber	—	Yin, Hay & Roth
Typ	NLP sentence-pair classification task	NLP text-classification task
Wegweisende Quelle	Dagan, I., Glickman, O. & Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge.	Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923.
Aliasnamen	natural language inference, NLI, recognising textual entailment, RTE	zero-shot text classification, entailment-based classification, Sıfır Atışlı Sınıflandırma (Zero-Shot Classification)
Verwandt	4	3
Zusammenfassung	Textual entailment, also known as natural language inference (NLI), is the natural-language-processing task of deciding whether one piece of text (the premise) entails a second piece of text (the hypothesis), contradicts it, or is neutral with respect to it. Formalised by the PASCAL Recognising Textual Entailment Challenge (Dagan, Glickman & Magnini, 2006) and broadened by the MultiNLI corpus (Williams, Nangia & Bowman, 2018), it underpins question answering and fact-verification pipelines.	Zero-shot classification is a natural-language-processing task that assigns text to categories described in plain language without requiring any labelled training data. Formalised as an entailment problem by Yin, Hay and Roth (2019), it lets a large pretrained language model recognise new categories on the fly simply by naming them, enabling rapid adaptation to fresh label sets.