A Confidence Model for Syntactically-Motivated Entailment Proofs

By Asher Stern, Ido Dagan. 2011

50 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text (2013, methodology): “For RTE, we used BIUTEE (Stern and Dagan, 2011), which performs better than the median system in the past four RTE competitions.”
Semantic Parsing via Paraphrasing (2014, methodology): “The NLP paraphrase literature is vast and ranges from simple methods employing surface features (Wan et al., 2006), through vector space models (Socher et al., 2011), to latent variable models (Das and Smith, 2009; Wang and Manning, 2010; Stern and Dagan, 2011).”
Can NLI Models Verify QA Systems' Predictions? (2021, background): “(2013) explore a similar pipeline, but find that it works quite poorly, possibly due to the low performance of entailment systems at the time (Stern and Dagan, 2011).”
Semantic Annotation for Textual Entailment Recognition (2012, methodology): “The function that assigns confidence values to rules and on-the-fly operations, and the threshold for predicting a valid/invalid entailment are learned automatically based on the training set (see [30] for full details).”
Design and realization of a modular architecture for textual entailment (2013, methodology): “EDAs: The platform covers the three systems whose mappings were outlined in the previous section: a transformation-based EDA (BIUTEE), a single-level classification EDA (EDITS), and a multi-level classification EDA (TIE).”
SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge (2013, methodology): “The UKP-BIU system is a hybrid of two semantic relationship approaches, namely (i) computing semantic textual similarity by combining multiple content similarity measures (B¨ar et al., 2012), and (ii) recognizing textual entailment with BIUTEE (Stern and Dagan, 2011).”
UKP: Computing Semantic Textual Similarity by Combining Multiple Content Similarity Measures (2012, methodology): “Textual Entailment We experimented with using the BIUTEE textual entailment system (Stern and Dagan, 2011) for generating entailment scores to serve as features for the classifier.”
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity (2019, methodology): “Unlike existing work on natural logic, however, our model avoids the need for having expensive alignment and search sub-procedures (MacCartney et al., 2008; Stern and Dagan, 2011), and relies on a much smaller set of background knowledge and primitive relations than MacCartney and Manning (2009).”
The Eras and Trends of Automatic Short Answer Grading (2015, methodology): “) is based on combining multiple textual similarity measures together using DKPro Similarity (Bär et al. 2013) and BIUTEE (Stern and Dagan 2011) as established technology for textual similarity and entailment respectively.”
Prerequisite Skills for Reading Comprehension: Multi-Perspective Analysis of MCTest Datasets and Systems (2017, background): “The third (Smith RTE) is an extension of the second that recognizes textual entailment (RTE) (Stern and Dagan 2011).”
Machine Comprehension with Discourse Relations (2015, methodology): “The second baseline, RTE , uses a textual entailment recognizer (Stern and Dagan, 2011) to determine if the answer (turned into a statement along with the question) is entailed by the passage.”
Knowledge-Based Textual Inference via Parse-Tree Transformations (2015, background): “Stern and Dagan (2011) and Stern, Stern, Dagan, and Felner (2012) extended our work to address these two aspects, respectively.”
Efficient Search for Transformation-based Inference (2012, methodology): “An appealing approach to such textual inferences is to explicitly transform T into H, using a sequence of transformations (Bar-Haim et al., 2007; Harmeling, 2009; Mehdad, 2009; Wang and Manning, 2010; Heilman and Smith, 2010; Stern and Dagan, 2011).”