Natural Logic and Natural Language Inference

41 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Tailor: Generating and Perturbing Text with Semantic Controls (2021, methodology): “For example, if used with WordNet (Miller, 1998), CHANGE_CONTENT can recreate perturbations that contain natural logic (MacCartney and Manning, 2014): In Table 3, doctor)adult creates an entailment relationship between the original and perturbed sentence, with “doctor” being a hyponym of “adult.”
PRover: Proof Generation for Interpretable Reasoning over Rules (2020, background): “Our task also bears similarity with Natural Language Inference (MacCartney and Manning, 2014), but NLI also allows unsupported inferences by filling gaps in explicitly stated knowledge (Dagan et al.”
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning (2021, methodology): “The task of rule reasoning (Clark et al., 2020) is related to other recently proposed tasks on QA (We-ston et al., 2015; Yang et al., 2018; Lin et al., 2019; Tafjord et al., 2019; Richardson et al., 2020) and NLI (MacCartney and Manning, 2014).”
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples (2018, methodology): “For instance, consider the 1 The symbols are based on Natural Logic (Lako ﬀ , 1970) and use the notation of MacCartney and Manning (2012). generator for ρ : = hypernym(car, vehicle) with the (partial) transformation function f ρ : = “Replace car with vehicle ” and the label g ρ : = entails . f ρ…”