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Detecting sarcasm and verbal irony is critical for understanding people's actual sentiments and beliefs.
Attentional encoder network for targeted sentiment classification
Youwei Song, Jiahai Wang, Tao Jiang, Zhiyue Liu, and Yanghui Rao. 2019 · 1902
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
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Debanjan Ghosh, Elena Musi, Kartikeya Upasani, and Smaranda Muresan. 2019 · 1911
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Semi-supervised recognition of sarcastic sentences in twitter and amazon
Dmitry Davidov, Oren Tsur, and Ari Rappoport. 2010 · 2010
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Icwsm-a great catchy name: Semi-supervised recognition of sarcastic sentences in online product reviews
Oren Tsur, Dmitry Davidov, and Ari Rappoport. 2010 · 2010
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Detecting ironic intent in creative comparisons
Tony Veale and Yanfen Hao. 2010 · 2010
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Identifying sarcasm in twitter: A closer look
Roberto González-Ibáñez, Smaranda Muresan, and Nina Wacholder. 2011 · 2011
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The perfect solution for detecting sarcasm in tweets# not
CC Liebrecht, FA Kunneman, and APJ van den Bosch. 2013 · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Sarcasm as contrast between a positive sentiment and negative situation
Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. 2013 · 2013
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Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis
Diana Maynard and Mark A Greenwood. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Humans require context to infer ironic intent (so computers probably do, too)
Byron C Wallace, Do Kook Choe, Laura Kertz, and Eugene Charniak. 2014 · 2014
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Contextualized sarcasm detection on twitter
David Bamman and Noah A Smith. 2015 · 2015
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Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words
Debanjan Ghosh, Weiwei Guo, and Smaranda Muresan. 2015 · 2015
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Harnessing context incongruity for sarcasm detection
Aditya Joshi, Vinita Sharma, and Pushpak Bhattacharyya. 2015 · 2015
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Your sentiment precedes you: Using an author’s historical tweets to predict sarcasm
Anupam Khattri, Aditya Joshi, Pushpak Bhattacharyya, and Mark Carman. 2015 · 2015
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Sarcasm detection on twitter: A behavioral modeling approach
Ashwin Rajadesingan, Reza Zafarani, and Huan Liu. 2015 · 2015
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Computational irony: A survey and new perspectives
Byron C Wallace. 2015 · 2015
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Twitter sarcasm detection exploiting a context-based model
Zelin Wang, Zhijian Wu, Ruimin Wang, and Yafeng Ren. 2015 · 2015
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Modelling context with user embeddings for sarcasm detection in social media
Silvio Amir, Byron C Wallace, Hao Lyu, and Paula Carvalho Mário J Silva. 2016 · 2016
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Fracking sarcasm using neural network
Aniruddha Ghosh and Tony Veale. 2016 · 2016
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Harnessing sequence labeling for sarcasm detection in dialogue from tv series ‘friends’
Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, and Mark Carman. 2016 · 2016
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Harnessing cognitive features for sarcasm detection
Abhijit Mishra, Diptesh Kanojia, Seema Nagar, Kuntal Dey, and Pushpak Bhattacharyya. 2016 · 2016
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Identification of nonliteral language in social media: A case study on sarcasm
Smaranda Muresan, Roberto Gonzalez-Ibanez, Debanjan Ghosh, and Nina Wacholder. 2016 · 2016
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Detecting sarcasm in multimodal social platforms
Rossano Schifanella, Paloma de Juan, Joel Tetreault, and Liangliang Cao. 2016 · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
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Semeval-2018 task 3: Irony detection in english tweets
Cynthia Van Hee, Els Lefever, and Véronique Hoste. 2018 · 2018
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Multi-modal sarcasm detection in twitter with hierarchical fusion model
Yitao Cai, Huiyu Cai, and Xiaojun Wan. 2019 · 2019
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Towards multimodal sarcasm detection (an _obviously_ perfect paper)
Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, and Soujanya Poria. 2019 · 2019
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Sentiment and sarcasm classification with multitask learning
Navonil Majumder, Soujanya Poria, Haiyun Peng, Niyati Chhaya, Erik Cambria, and Alexander Gelbukh. 2019 · 2019
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Exploring author context for detecting intended vs perceived sarcasm
Silviu Oprea and Walid Magdy. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
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Meishan Zhang, Yue Zhang, and Guohong Fu. 2016 · 2016
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Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes. 2017 · 2017
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Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, and Sune Lehmann. 2017 · 2017
Cited alongside, same era.
Magnets for sarcasm: Making sarcasm detection timely, contextual and very personal
Aniruddha Ghosh and Tony Veale. 2017 · 2017
Cited alongside, same era.
The role of conversation context for sarcasm detection in online interactions
Debanjan Ghosh, Alexander Richard Fabbri, and Smaranda Muresan. 2017 · 2017
Cited alongside, same era.
Automatic sarcasm detection: A survey
Aditya Joshi, Pushpak Bhattacharyya, and Mark J Carman. 2017 · 2017
Cited alongside, same era.
A large self-annotated corpus for sarcasm
Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2017 · 2017
Cited alongside, same era.
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Lcf: A local context focus mechanism for aspect-based sentiment classification
Biqing Zeng, Heng Yang, Ruyang Xu, Wu Zhou, and Xuli Han. 2019 · 2019
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Sarcasm identification and detection in conversion context using BERT
kalaivani A and Thenmozhi D. 2020 · 2020
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Applying Transformers and aspect-based sentiment analysis approaches on sarcasm detection
Taha Shangipour ataei, Soroush Javdan, and Behrouz Minaei-Bidgoli. 2020 · 2020
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Detecting sarcasm in conversation context using Transformer based model
Adithya Avvaru, Sanath Vobilisetty, and Radhika Mamidi. 2020 · 2020
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Context-aware sarcasm detection using BERT
Arup Baruah, Kaushik Das, Ferdous Barbhuiya, and Kuntal Dey. 2020 · 2020
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Sarcasm detection using context separators in online discourse
Tanvi Dadu and Kartikey Pant. 2020 · 2020
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Transformer-based context-aware sarcasm detection in conversation threads from social media
Xiangjue Dong, Changmao Li, and Jinho D. Choi. 2020 · 2020
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A Transformer approach to contextual sarcasm detection in twitter
Hunter Gregory, Steven Li, Pouya Mohammadi, Natalie Tarn, Rachel Ballantyne, and Cynthia Rudin. 2020 · 2020
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Neural sarcasm detection using conversation context
Nikhil Jaiswal. 2020 · 2020
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C-net: Contextual network for sarcasm detection
Amit Kumar Jena, Aman Sinha, and Rohit Agarwal. 2020 · 2020
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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Sarcasm detection in tweets with BERT and GloVe embeddings
Akshay Khatri and Pranav P. 2020 · 2020
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Transformers on sarcasm detection with context
Amardeep Kumar and Vivek Anand. 2020 · 2020
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Augmenting data for sarcasm detection with unlabeled conversation context
Hankyol Lee, Youngjae Yu, and Gunhee Kim. 2020 · 2020
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Sarcasm detection using an ensemble approach
Jens Lemmens, Ben Burtenshaw, Ehsan Lotfi, Ilia Markov, and Walter Daelemans. 2020 · 2020
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A novel hierarchical BERT architecture for sarcasm detection
Himani Srivastava, Vaibhav Varshney, Surabhi Kumari, and Saurabh Srivastava. 2020 · 2020
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