Book Reviews: Recognizing Textual Entailment: Models and Applications by Ido Dagan, Dan Roth, Mark Sammons and Fabio Massimo Zanzotto

By Ido Dagan, Dan Roth, Mark Sammons, Fabio Massimo Zanzotto. 2013

126 citations recorded by the source corpus.

* List of Figures* List of Tables* Preface* Acknowledgments* Textual Entailment* Architectures and Approaches* Alignment, Classification, and Learning* Case Studies* Knowledge Acquisition for Textual Entailment* Research Directions in RTE* Bibliography* Authors' Biographies

Subfields: Natural Language Processing

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Citations in the corpus

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021, background): “Yet, we are also different from Natural Language Inference (NLI) and textual entailment, which work with text directly, but cannot handle Horn rules (MacCartney and Manning, 2009; Dagan et al., 2013).”
Learning Natural Language Inference with LSTM (2015, background): “A comprehensive review on existing work can be found in the book by Dagan et al. (2013).”
Natural Language to Structured Query Generation via Meta-Learning (2018, background): “For instance, recognizing textual entailment is a binary classification problem on whether the hypothesis follows a given textual statement, but the challenge datasets consist of a huge variety of inference categories and genres (Dagan et al., 2013; Williams et al., 2017).”
A Natural Proof System for Natural Language (2017, methodology): “In fact, knowledge acquisition is a major bottleneck in RTE (Dagan et al., 2013, p. 7).”
Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge (2020, background): “, [21, 22, 23, 24], where models learn to score a hypothesis but in an opaque and somewhat unpredictable way [25, 26].”
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering (2020, methodology): “Following the textual entailment literature (Dagan et al., 2013), we define entailment using human judgements rather than formally, i.”
A question-entailment approach to question answering (2019, methodology): “ed work, the meaning associated with Natural Language Inference (NLI) varies among different tasks and events. For instance, Recognizing Textual Entailment (RTE) was addressed by the PASCAL challenge [13], where the entailment relation has been assessed manually by human judges who selected relevant”
The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability (2020, methodology): “2 Measuring Bias With Inference Our construction of a measure of bias uses the NLI task, which has been widely studied in NLP, starting with the PASCAL RTE challenges [21, 22].”
Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution (2019, background): “Numeric expressions play an important role in various tasks, including textual entailment (Lev et al., 2004; Dagan et al., 2013), solving arithmetic problems (Roy and Roth, 2015), numeric reasoning (Roy et al., 2015; Trask et al., 2018) and language modeling (Spithourakis and Riedel, 2018).”
A Logic-Driven Framework for Consistency of Neural Models (2019, background): “To illustrate this notion of consistency, let us consider the task of natural language inference (NLI) which seeks to identify whether a premise entails, contradicts or is unrelated to a hypothesis (Dagan et al., 2013).”
Detecting Asymmetric Semantic Relations in Context: A Case-Study on Hypernymy Detection (2017, background): “Lexical entailment or hypernymy in context is also different from recognizing textual entailment (RTE).”
LAMBADA: Backward Chaining for Automated Reasoning in Natural Language (2022, methodology): “In this sense, models trained to perform NLI are also relevant, although inferences under NLI adopt a more relaxed notion of entailment rather than purely logical (Dagan et al., 2013; Bowman et al., 2015; Williams et al., 2018).”
Automated Historical Fact-Checking by Passage Retrieval, Word Statistics, and Virtual Question-Answering (2017, background): “Textual entailment recognition (RTE) (Dagan et al., 2013) has been extensively studied in the field of language processing. RTE can be seen as a quite restricted form of fact-checking where two sentences t and h are given and a system judges whether t is an evidence of (i.e., it entails) h or not. T”
Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations (2018, unknown): “We extend various prior works on challenge datasets, 1The task of determining whether a human would likely infer a textual hypothesis from a context, or premise (Dagan et al., 2006, 2013). e.g. Zhang et al. (2017) and White et al. (2017).”