Entailment, intensionality and text understanding

By Cleo Condoravdi, Dick Crouch, Valeria C V de Paiva, Reinhard Stolle, D. Bobrow. 2003

127 citations recorded by the source corpus.

We argue that the detection of entailment and contradiction relations between texts is a minimal metric for the evaluation of text understanding systems. Intensionality, which is widespread in natural language, raises a number of detection issues that cannot be brushed aside. We describe a contexted clausal representation, derived from approaches in formal semantics, that permits an extended range of intensional entailments and contradictions to be tractably detected.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

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

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference (2017, background): “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…”
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference (2019, background): “, implies the truth of) a hypothesis sentence (Condoravdi et al., 2003; Dagan et al., 2006; Bowman et al., 2015).”
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].”
Hypothesis Only Baselines in Natural Language Inference (2018, methodology): “Starting with Dagan et al. (2006), there have been eight iterations of the PASCAL RTE challenge with the most recent being Dzikovska et al.”
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].”
Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment (2018, methodology): “This paper proposes a new empirical method to investigate a quality of a large corpus designed for the recognizing textual entailment (RTE) task (Condoravdi et al., 2003; Bos and Markert, 2005; MacCartney and Manning, 2009; Marelli et al., 2014a).”
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance (2019, background): “(NLI, also known as Recognizing Textual Entailment; Condoravdi et al., 2003; Dagan et al., 2006, 2013), which involves giving a model two sentences, called the premise and the hypothesis.”
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference (2019, background): “guage understanding is natural language inference (NLI)(or recognizing textual entailment (RTE)) (Cooper et al., 1996; Condoravdi et al., 2003; Bos and Markert, 2005; Dagan et al., 2006), wherein a model determines if a natural language hypothesis”
Non-entailed subsequences as a challenge for natural language inference (2018, background): “Introduction: Natural language inference (NLI) — the task of determining whether a premise entails a hypothesis — is a central challenge for natural language understanding systems (Condoravdi et al., 2003; Dagan et al., 2006; Bowman et al., 2015).”
Finding Contradictions in Text (2008, background): “Condoravdi et al. (2003) ﬁrst recognized the importance of handling both entailment and contradiction for text understanding: “relations of entailment and contradiction are the key data of semantics, as traditionally viewed as a branch of linguistics.”
Language Tasks and Language Games: On Methodology in Current Natural Language Processing Research (2019, background): “About the natural language inference problem, and attempting at least an implicit structuring of the space of capabilities, Condoravdi et al. (2003) write: “The ability to recognize such semantic relations is clearly not a sufficient criterion for language understanding: there is more to language…”
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.e.,…”
UnNatural Language Inference (2020, unknown): “We focus here on textual entailment, one of the hallmark tasks used to measure how well models understand language (Condoravdi et al., 2003; Dagan et al., 2005).”
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…”
Recognizing textual entailment: Rational, evaluation and approaches – Erratum (2010, background): “Recently there have been just a few suggestions in the literature to regard entailment recognition for texts as an applied, empirically evaluated, task (see Condoravdi et al. 2003; Dagan and Glickman 2004; Monz and de Rijke 2001).”