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This paper describes a baseline for the second iteration of the Fact Extraction and VERification shared task (FEVER2.0) which explores the resilience of systems through adversarial evaluation.
Generating Natural Adversarial Examples
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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
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Enhanced LSTM for Natural Language Inference
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Evaluating the morphological competence of Machine Translation Systems
Franck Burlot and François Yvon · 2017
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Towards linguistically generalizable nlp systems: A workshop and shared task
Allyson Ettinger, Sudha Rao, Hal Daumé III, and Emily M Bender · 2017
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AllenNLP: A Deep Semantic Natural Language Processing Platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer · 2017
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Annotation Artifacts in Natural Language Inference Data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith · 2017
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A challenge set approach to evaluating machine translation
Pierre Isabelle, Colin Cherry, and George Foster · 2017
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Adversarial Examples for Evaluating Reading Comprehension Systems
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Breaking nlp: Using morphosyntax, semantics, pragmatics and world knowledge to fool sentiment analysis systems
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Adversarial training methods for semi-supervised text classification
Team papelo: Transformer networks at fever
Christopher Malon · 2018
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Stress Test Evaluation for Natural Language Inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Hypothesis Only Baselines in Natural Language Inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme · 2018
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Semantically Equivalent Adversarial Rules for Debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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The Fact Extraction and VERification (FEVER) Shared Task
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal
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Automated Fact Checking: Task formulations, methods and future directions
James Thorne and Andreas Vlachos · 2018
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FEVER: a large-scale dataset for Fact Extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal · 2018
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Ucl machine reading group: Four factor framework for fact finding (hexaf)
Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel · 2018
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SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi · 2018
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Combining fact extraction and verification with neural semantic matching networks
Yixin Nie, Haonan Chen, and Mohit Bansal · 2019
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