Textual Entailment
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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Dagan, I., Glickman, O. & Magnini, B. (2006). The PASCAL Recognising Textual Entailment Challenge. · URL
- Williams, A., Nangia, N. & Bowman, S. (2018). A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. NAACL. · URL
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.