Recognizing textual entailment: Rational, evaluation and approaches

By Ido Dagan, Bill Dolan, Bernardo Magnini, Dan Roth. 2009

230 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

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Citations in the corpus

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation (2017, background): “Semantic inference tasks related to STS include textual entailment (Bentivogli et al., 2016; Bowman et al., 2015; Dagan et al., 2010), semantic relatedness (Bentivogli et al., 2016) and paraphrase detection (Xu et al., 2015; Ganitkevitch et al., 2013; Dolan et al., 2004).”
FEVER: a Large-scale Dataset for Fact Extraction and VERification (2018, background): “When compared to textual entailment (TE)/natural language inference (Dagan et al., 2009; Bowman et al., 2015), the key difference is that in these tasks the passage to verify each claim is given, and in recent years it typically consists a single sentence, while in veri-ﬁcation systems it is…”
Stress Test Evaluation for Natural Language Inference (2018, background): “Due to its importance, significant prior work (Dagan et al., 2006; Dagan et al., 2009; Dagan et al., 2013; Marelli et al., 2014a) has focused on developing datasets and models for this benchmark task.”
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation (2016, background): “The operational approach to sentence level semantics was popularized by the recognizing textual entailment task (Dagan et al., 2010).”
SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals (2009, methodology): “This is in the tradition of the Textual Entailment or Information Validation paradigm (Dagan et al., 2009), and in contrast to “aboutness” annotation such as semantic roles (Carreras and Màrquez, 2004) or the BioNLP 2009 task (Kim et al.”
The Fact Extraction and VERification (FEVER) Shared Task (2018, background): “A key difference between this task and other textual entailment and natural language inference tasks (Dagan et al., 2009; Bowman et al., 2015) is the need to identify the evidence from a large textual corpus.”
Read + Verify: Machine Reading Comprehension with Unanswerable Questions (2018, background): “ Our no-answer reader extends existing approaches by introducing two auxiliary losses that enhance these two tasks independently. Recognizing Textual Entailment. Recognizing textual entailment (RTE) (Dagan et al. 2010; Marelli et al. 2014), or known as natural language inference (NLI) (Bowman et al.”
Automated Fact Checking: Task Formulations, Methods and Future Directions (2018, background): “Ferreira and Vlachos (2016) modeled fact checking as a form of Recognizing Textual Entailment (RTE) (Dagan et al., 2009; Bowman et al., 2015), predicting whether a premise, typically (part of) a news article, is for , against , or observing a given claim.”
The Excitement Open Platform for Textual Inferences (2014, background): “In the last decade textual entailment (Dagan et al., 2009) has been a very active topic in Computational Linguistics, providing a unifying framework for textual inference.”
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants (2017, background): “In particular, the goal of the semantic inference task textual entailment is to classify whether a proposition entails or contradicts a hypothesis (Dagan et al. 2009).”
Lessons from Natural Language Inference in the Clinical Domain (2018, background): “Moreover, contribution of datasets from past shared tasks (Dagan et al., 2009), and recent research (Bowman et al., 2015; Williams et al., 2018) have pushed the boundaries for this seemingly simple, but challenging problem.”
Learning Entailment Relations by Global Graph Structure Optimization (2012, background): “The concept of entailment was explained under the framework of TE (Dagan et al. 2009), that is, the template t 1 entails the template t 2 if given that the instantiation of t 1 with some concept is true then the instantiation of t 2 with the same concept is most likely true.”
A Survey of Paraphrasing and Textual Entailment Methods (2009, background): “There are 200 training and 200 testing pairs for each scenario; Dagan et al. (2009) explain how they were constructed.”
SemEval-2018 Task 12: The Argument Reasoning Comprehension Task (2018, background): “Previous tasks have dealt with the textual entailment of a hypothesis from a proposition (Dagan et al., 2009) or with semantic inference (Bowman et al., 2015).”
Evidence-based Verification for Real World Information Needs (2021, background): “Evidence The use of evidence in fact veriﬁcation allows the task to be modeled as recognizing textual entailment (Dagan et al., 2009) – predicting whether a claim can be supported or refuted from evidence.”