A Natural Logic Inference System

By Yaroslav Fyodorov, YoadWinter NissimFrancez. 2000

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In Natural Language Processing

Citations in the corpus

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference (2017, methodology): “In this task, also known as recognizing textual entailment (RTE; Fyodorov et al., 2000; Condoravdi et al., 2003; Bos and Markert, 2005; Dagan et al., 2006; MacCartney and Manning, 2009), a model is presented with a pair of sentences—like one of those in Figure 1— and asked to judge the relationship…”
A large annotated corpus for learning natural language inference (2015, background): “Thus, natural language inference (NLI) — characterizing and using these relations in computational systems (Fyodorov et al., 2000; Condoravdi et al., 2003; Bos and Markert, 2005; Dagan et al., 2006; MacCartney and Manning, 2009) — is essential in tasks ranging from information retrieval to semantic ”
The Natural Language Decathlon: Multitask Learning as Question Answering (2018, background): “Natural Language Inference NLI has a long history playing roles in tasks like information retrieval and semantic parsing [Fyodorov et al., 2000, Condoravdi et al., 2003, Bos and Markert, 2005, Dagan et al., 2005, MacCartney and Manning, 2009].”
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021, methodology): “Natural Language Inference: ANLI R1, ANLI R2, ANLI R3 (Fyodorov et al., 2000)”
Visual Entailment: A Novel Task for Fine-Grained Image Understanding (2019, methodology): “We focus on textual entailment as our NLI task [18, 11, 3, 12, 46].”
An extended model of natural logic (2009, background): “theoretical aspects of natural logic, see (7, 18, 23).”
Natural Logic and Natural Language Inference (2014, background): “For recent work on theoretical aspects of natural logic, see [7, 18, 23].”
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge (2018, background): “NLI, also referred to as Recognising Textual Entailment (Fyodorov et al., 2000; Condoravdi et al., 2003; Dagan et al., 2005), is a central problem in language understanding (Katz, 1972; Bos and Markert, 2005; van Benthem, 2008; MacCartney and Manning, 2009), and thus it is especially well suited to…”
ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning (2020, background): “The task of natural language inference, also known as recognizing textual entailment (Fyodorov et al., 2000; Condoravdi et al., 2003; Bos & Markert, 2005; Dagan et al., 2005; MacCartney & Manning, 2009) requires models to take a pair of sentence as input and classify their relationship types, i.”
Differentiable Reasoning on Large Knowledge Bases and Natural Language (2019, background): “A rich and broad literature in MR has approached this problem within a variety of frameworks, including Natural Logic (MacCartney and Manning 2007), Semantic Parsing (Bos 2008), Natural Language Inference and Recognising Textual Entailment (Fyodorov, Winter, and Francez 2000; Bowman et al. 2015), an”