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Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search.
The argument reasoning comprehension task: Identification and reconstruction of implicit warrants
Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, and Benno Stein. 2018 · 1940
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Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel
J. Peter Kincaid, Robert P. Fishburne Jr., Richard L. Rogers, and Brad S. Chissom. 1975 · 1975
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Get out the vote: Determining support or opposition from congressional floor-debate transcripts
Matt Thomas, Bo Pang, and Lillian Lee. 2006 · 2006
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Recognizing stances in ideological on-line debates
Swapna Somasundaran and Janyce Wiebe. 2010 · 2010
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Stance classification of ideological debates: Data, models, features, and constraints
Kazi Saidul Hasan and Vincent Ng. 2013 · 2013
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Emergent: a novel data-set for stance classification
William Ferreira and Andreas Vlachos. 2016 · 2016
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Semeval-2016 task 6: Detecting stance in tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. 2016 · 2016
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A survey of transfer learning
Karl Weiss, Taghi M Khoshgoftaar, and DingDing Wang. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
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Stance classification of context-dependent claims
Roy Bar-Haim, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, and Noam Slonim. 2017 · 2017
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Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2017 · 2017
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Semeval-2017 task 8: Rumoureval: Determining rumour veracity and support for rumours
Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Arkaitz Zubiaga. 2017 · 2017
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A challenge set approach to evaluating machine translation
Pierre Isabelle, Colin Cherry, and George Foster. 2017 · 2017
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OpenNMT: Open-source toolkit for neural machine translation
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017 · 2017
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Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems
Taylor Mahler, Willy Cheung, Micha Elsner, David King, Marie-Catherine de Marneffe, Cory Shain, Symon Stevens-Guille, and Michael White. 2017 · 2017
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Paraphrasing revisited with neural machine translation
Jonathan Mallinson, Rico Sennrich, and Mirella Lapata. 2017 · 2017
Cited alongside, same era.
Where the truth lies: Explaining the credibility of emerging claims on the web and social media
Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2017 · 2017
Cited alongside, same era.
An overview of multi-task learning in deep neural networks
Sebastian Ruder. 2017 · 2017
Cited alongside, same era.
Learning to select data for transfer learning with Bayesian optimization
Sebastian Ruder and Barbara Plank. 2017 · 2017
Cited alongside, same era.
A dataset for multi-target stance detection
Parinaz Sobhani, Diana Inkpen, and Xiaodan Zhu. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Seeing things from a different angle:discovering diverse perspectives about claims
Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, and Dan Roth. 2019 · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019 · 2019
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Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets
Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019 · 2019
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Semeval-2019 task 7: Rumoureval, determining rumour veracity and support for rumours
Genevieve Gorrell, Ahmet Aker, Kalina Bontcheva, Leon Derczynski, Elena Kochkina, Maria Liakata, and Arkaitz Zubiaga. 2019 · 2019
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A richly annotated corpus for different tasks in automated fact-checking
Andreas Hanselowski, Christian Stab, Claudia Schulz, Zile Li, and Iryna Gurevych. 2019 · 2019
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Breaking nli systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018
Cited alongside, same era.
A retrospective analysis of the fake news challenge stance-detection task
Andreas Hanselowski, Avinesh PVS, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, and Iryna Gurevych. 2018 · 2018
Cited alongside, same era.
Adversarially regularising neural nli models to integrate logical background knowledge
Pasquale Minervini and Sebastian Riedel. 2018 · 2018
Cited alongside, same era.
Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
Cited alongside, same era.
Multi-task learning for argumentation mining in low-resource settings
Claudia Schulz, Steffen Eger, Johannes Daxenberger, Tobias Kahse, and Iryna Gurevych. 2018 · 2018
Cited alongside, same era.
Cross-topic argument mining from heterogeneous sources
Christian Stab, Tristan Miller, Benjamin Schiller, Pranav Rai, and Iryna Gurevych. 2018 · 2018
Cited alongside, same era.
Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang. 2019 · 2019
Later among the works it cites.
Using machine learning for stance detection
Yan Jiang. 2019 · 2019
Later among the works it cites.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019 · 2019
Later among the works it cites.
Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao. 2019 · 2019
Later among the works it cites.
STANCY: Stance classification based on consistency cues
Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2019 · 2019
Later among the works it cites.
Sampling bias in deep active classification: An empirical study
Ameya Prabhu, Charles Dognin, and Maneesh Singh. 2019 · 2019
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Combating adversarial misspellings with robust word recognition
Danish Pruthi, Bhuwan Dhingra, and Zachary C. Lipton. 2019 · 2019
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Evaluating adversarial attacks against multiple fact verification systems
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2019 · 2019
Later among the works it cites.
BLCU_NLP at SemEval-2019 task 7: An inference chain-based GPT model for rumour evaluation
Ruoyao Yang, Wanying Xie, Chunhua Liu, and Dong Yu. 2019 · 2019
Later among the works it cites.
The Fake News Challenge: Exploring how artificial intelligence technologies could be leveraged to combat fake news
Dean Pomerleau and Delip Rao. 2017 · 2020
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