“Ask Not What Textual Entailment Can Do for You...”

By Mark Sammons, V.G.Vinod Vydiswaran, Dan Roth. 2010

How researchers see this paper

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

In Natural Language Processing

Citations in the corpus

Reasoning about Quantities in Natural Language (2015, methodology): “…have been recognized as an important part of a textual entailment system (de Marneffe et al., 2008; Maccartney and Manning, 2008; Garoufi, 2007; Sammons et al., 2010), and (de Marneffe et al., 2008) claims that discrepancies in numbers are a common source of contradictions in natural language…”
What Makes Reading Comprehension Questions Easier? (2018, methodology): “Sugawara et al. (2017) also organized 10 prerequisite skills for MRC. LoBue and Yates (2011) and Sammons et al. (2010) analyzed entailment phenomena using detailed classifications in RTE.”
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference (2019, background): “These requirements show a reciprocity: NLI lends itself as a test bed for quantitative reasoning, which conversely, is important for NLI (Sammons et al., 2010; Clark, 2018).”
Crowdsourcing Inference-Rule Evaluation (2012, background): “These issues have also been noted by Sammons et al. (2010) and LoBue and Yates (2011).”
Divide and Conquer: Crowdsourcing the Creation of Cross-Lingual Textual Entailment Corpora (2011, background): “We believe that, in the same spirit of recent works promoting large-scale annotation efforts around entailment corpora (Sammons et al., 2010; Bentivogli et al., 2010), the proposed approach and the resulting dataset5 will contribute to meeting the strong need for resources to develop and evaluate no”
Semantic Annotation for Textual Entailment Recognition (2012, result): “Our annotation work is to a large extent in line with the proposal described in [28], whose authors appeal to the NLP community to contribute to entailment recognition work by incrementally annotating the phenomena that underlie the inferences in the RTE corpus.”
CogCompNLP: Your Swiss Army Knife for NLP (2018, background): “This library has been successfully used to facilitate the feature extraction for several higher level NLP applications like Semantic Role Labeling (Punyakanok et al., 2005), Coreference resolution (Rizzolo and Roth, 2016) and, Textual Entailment (Sammons et al., 2010), which use information across s”
Lessons from Natural Language Inference in the Clinical Domain (2018, background): “s of the same numerical value.11 The ﬁrst step is to learn what values are abnormal and the next is to actually perform the inference. This has been identiﬁed as a major challenge for NLI since long (Sammons et al., 2010). Many inferences require world knowledge that could be deemed close to open do”
Prerequisite Skills for Reading Comprehension: Multi-Perspective Analysis of MCTest Datasets and Systems (2017, background): “Sammons, Vydiswaran, and Roth (2010) proposed a methodology for robust development to recognize textual entailment.”
ANLIzing the Adversarial Natural Language Inference Dataset (2020, methodology): “Our scheme can handle nearly all of the suggested reasoning types in Sammons et al. (2010).”