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The widely studied task of Natural Language Inference (NLI) requires a system to recognize whether one piece of text is textually entailed by another, i.e.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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The Hungarian method for the assignment problem
Harold W Kuhn. 1955 · 1955
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Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
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From TreeBank to PropBank
Paul R Kingsbury and Martha Palmer. 2002 · 1993
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The Berkeley FrameNet project
Collin F Baker, Charles J Fillmore, and John B Lowe. 1998 · 1998
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Probabilistic textual entailment: Generic applied modeling of language variability
I. Dagan and O. Glickman. 2004 · 2004
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The NomBank project: An interim report
Adam Meyers, Ruth Reeves, Catherine Macleod, Rachel Szekely, Veronika Zielinska, Brian Young, and Ralph Grishman. 2004 · 2004
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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The proposition bank: An annotated corpus of semantic roles
Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005 · 2005
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Finding contradictions in text
Marie-Catherine de Marneffe, Anna N. Rafferty, and Christopher D. Manning. 2008 · 2008
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Open information extraction from the web
Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S Weld. 2008 · 2008
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Semantic role labeling
Martha Palmer, Daniel Gildea, and Nianwen Xue. 2010 · 2010
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ClausIE: clause-based open information extraction
Luciano Del Corro and Rainer Gemulla. 2013 · 2013
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Recognizing partial textual entailment
Omer Levy, Torsten Zesch, Ido Dagan, and Iryna Gurevych. 2013 · 2013
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Recognizing implied predicate-argument relationships in textual inference
Asher Stern and Ido Dagan. 2014 · 2014
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Question-answer driven semantic role labeling: Using natural language to annotate natural language
Luheng He, Mike Lewis, and Luke Zettlemoyer. 2015 · 2015
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TensorFlow: a system for Large-Scale machine learning
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016 · 2016
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur. 2016 · 2016
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Neural open information extraction
Lei Cui, Furu Wei, and Ming Zhou. 2018 · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
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Controlled crowdsourcing for high-quality QA-SRL annotation
Paul Roit, Ayal Klein, Daniela Stepanov, Jonathan Mamou, Julian Michael, Gabriel Stanovsky, Luke Zettlemoyer, and Ido Dagan. 2020 · 2020
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Improving faithfulness in abstractive summarization with contrast candidate generation and selection
Sihao Chen, Fan Zhang, Kazoo Sone, and Dan Roth. 2021 · 2021
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Decontextualization: Making sentences stand-alone
Eunsol Choi, Jennimaria Palomaki, Matthew Lamm, Tom Kwiatkowski, Dipanjan Das, and Michael Collins. 2021 · 2021
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Get your vitamin C! robust fact verification with contrastive evidence
Tal Schuster, Adam Fisch, and Regina Barzilay. 2021 · 2021
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Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Cited alongside, same era.
Supervised open information extraction
Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, and Ido Dagan. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2019 · 2019
Cited alongside, same era.
On aligning OpenIE extractions with knowledge bases: A case study
Kiril Gashteovski, Rainer Gemulla, Bhushan Kotnis, Sven Hertling, and Christian Meilicke. 2020 · 2020
Cited alongside, same era.
Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett. 2020 · 2020
Cited alongside, same era.
Wenpeng Yin, Dragomir Radev, and Caiming Xiong. 2021 · 2021
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Generating literal and implied subquestions to fact-check complex claims
Jifan Chen, Aniruddh Sriram, Eunsol Choi, and Greg Durrett. 2022 · 2022
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CompactIE: Compact facts in open information extraction
Farima Fatahi Bayat, Nikita Bhutani, and H. Jagadish. 2022 · 2022
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QASem parsing: Text-to-text modeling of QA-based semantics
Ayal Klein, Eran Hirsch, Ron Eliav, Valentina Pyatkin, Avi Caciularu, and Ido Dagan. 2022 · 2022
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SummaC: Re-visiting NLI-based models for inconsistency detection in summarization
Philippe Laban, Tobias Schnabel, Paul N Bennett, and Marti A Hearst. 2022 · 2022
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Scaling up models and data with t5x and seqio
Adam Roberts, Hyung Won Chung, Anselm Levskaya, Gaurav Mishra, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, et al. 2022 · 2022
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Stretching sentence-pair NLI models to reason over long documents and clusters
Tal Schuster, Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, and Donald Metzler. 2022 · 2022
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Logical reasoning with span predictions: Span-level logical atoms for interpretable and robust nli models
Joe Stacey, Pasquale Minervini, Haim Dubossarsky, and Marek Rei. 2022 · 2022
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Large-scale QA-SRL parsing
Nicholas FitzGerald, Julian Michael, Luheng He, and Luke Zettlemoyer. 2018 · 2060
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