Recognizing Textual Entailment: Models and Applications Ido Dagan1, Dan Roth2, Mark Sammons2, and Fabio Massimo Zanzotto3 (1Bar-Ilan University, Israel, 2University of Illinois, Urbana, IL, and…

By Bernardo Magnini. 2015

222 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences (2018, methodology): “In particular, in natural language processing (NLP) it has been studied under various settings, such as multiplechoice Question-Answering (QA) (Green Jr. et al., 1961), Reading Comprehension (RC) (Hirschman et al., 1999), Recognizing Textual Entailment (RTE) (Dagan et al., 2013) etc.”
On Measuring and Mitigating Biased Inferences of Word Embeddings (2019, methodology): “Our construction of a bias measure uses the NLI task, which has been widely studied in NLP, starting with the PAS-CAL RTE challenges (Dagan, Glickman, and Magnini 2006; Dagan et al. 2013).”
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias (2022, methodology): “We fine-tuned T5 11 on RTE for 6 epochs with batch size 8.”
Reasoning Over Semantic-Level Graph for Fact Checking (2019, background): “Previous works are dominated by natural language inference models (Dagan et al., 2013; An-geli and Manning, 2014) because the task requires reasoning of the claim and retrieved evidence sentences.”
Transformers as Soft Reasoners over Language (2020, methodology): “It is also related to textual entailment and Natural Language Inference (NLI) [Manning and MacCartney, 2009], but with the important difference that NLI also allows unsupported inferences that “a person would typically infer” [Dagan et al., 2013].”
Reasoning about Quantities in Natural Language (2015, methodology): “Annotation Example for RTE sub-corpus T:A bomb in a Hebrew University cafeteria killed [five Americans] and [four Israelis].”
ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers (2022, methodology): “When ﬁne-tuning on GLUE [65] tasks ((i.e., MRPC [17], STS-B [10], SST-2 [58], QNLI [51], QQP [27], MNLI [70], CoLA [69], RTE [13]).”
Hypothesis Only Baselines in Natural Language Inference (2018, background): “Computationally, the task of NLI is to predict an entailment relation label (output) given a premise-hypothesis pair (input), i.e., to determine whether the truth of the hypothesis follows from the truth of the premise (Dagan et al., 2006, 2013).”
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences (2018, background): “Recognizing textual entailment (RTE) (Dagan et al., 2013), recently framed as natural language inference (NLI) (Bowman et al.”
Stress Test Evaluation for Natural Language Inference (2018, background): “NLI has been proposed as a benchmark task for natural language understanding research (Cooper et al., 1996; Dagan et al., 2006; Giampiccolo et al., 2007; Dagan et al., 2013; Bowman et al., 2015; Nangia et al., 2017), due to the requirement for models to reason about several difficult linguistic phen”
SciTaiL: A Textual Entailment Dataset from Science Question Answering (2018, background): “It is a central problem in natural language understanding (Dagan et al. 2013) as it encapsulates the fundamental challenge of linguistic variability.”
Semantic Parsing via Paraphrasing (2014, background): “A fundamental motivation and long standing goal of the paraphrasing and RTE communities has been to cast various semantic applications as para-phrasing/textual entailment (Dagan et al., 2013).”
Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System (2021, positive): “E NTAIL - MENT models the text classiﬁcation problem in a textual entailment (Dagan et al., 2013) framework.”
Unsupervised Commonsense Question Answering with Self-Talk (2020, background): “…across natural language understanding (NLU) tasks such as reading comprehension (Hirschman et al., 1999) and recognizing textual entailment (Dagan et al., 2013), and Alice let Bob stand in front of her at the concert. even more so in tasks dedicated to commonsense reasoning such as the…”
Question Answering via Integer Programming over Semi-Structured Knowledge (2016, background): “The task of Recognizing Textual Entailment (RTE) [Da-gan et al. , 2010; Dagan et al. , 2013 ] is also closely related, as QA can be cast as entailment (Does corpus entail question+answer ?”
Cross-Lingual Ability of Multilingual BERT: An Empirical Study (2019, methodology): “In this work, we conduct all our experiments on two conceptually different downstream tasks – cross-lingual Textual Entailment (TE) and cross-lingual Named Entity Recognition (NER).”
Explaining Answers with Entailment Trees (2021, methodology): “sion that “a person would typically infer” (Dagan et al., 2013)), i.”
Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets (2019, background): “To successfully recognize textual entailment (RTE; Dagan et al., 2013), also known as natural language inference (NLI) (MacCartney and Manning, 2008; Bowman et al.”
PRover: Proof Generation for Interpretable Reasoning over Rules (2020, background): “Our task also bears similarity with Natural Language Inference (MacCartney and Manning, 2014), but NLI also allows unsupported inferences by filling gaps in explicitly stated knowledge (Dagan et al., 2013).”
Focused Entailment Graphs for Open IE Propositions (2014, methodology): “A proposition-level entailment ( s i , s j ) holds if the verbalization of s i implies s j , according to the deﬁnition of textual entailment (Dagan et al., 2013); i.e. if humans reading s i would typically infer that s j is most likely true.”