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A growing number of state-of-the-art transfer learning methods employ language models pretrained on large generic corpora.
How universal and specific is emotional experience? evidence from 27 countries on five continents
Harald G. Wallbott and Klaus R. Scherer. 1986 · 1986
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Multitask learning: A knowledge-based source of inductive bias
Rich Caruana. 1993 · 1993
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Nltk: The natural language toolkit
Edward Loper and Steven Bird. 2002 · 2002
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston. 2008 · 2008
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011 · 2011
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Really? well. apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue
Stephanie Lukin and Marilyn Walker. 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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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015 · 2015
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Creating and characterizing a diverse corpus of sarcasm in dialogue
Shereen Oraby, Vrindavan Harrison, Lena Reed, Ernesto Hernandez, Ellen Riloff, and Marilyn A. Walker. 2016 · 2016
Cited alongside, same era.
Deep multi-task learning with low level tasks supervised at lower layers
Anders Sogaard and Yoav Goldberg. 2016 · 2016
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Unsupervised pretraining for sequence to sequence learning
Prajit Ramachandran, Peter Liu, and Quoc Le. 2017 · 2017
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Semeval-2017 task 4: Sentiment analysis in twitter
Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017 · 2017
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Ntua-slp at semeval-2018 task 3: Tracking ironic tweets using ensembles of word and character level attentive rnns
Christos Baziotis, Athanasiou Nikolaos, Pinelopi Papalampidi, Athanasia Kolovou, Georgios Paraskevopoulos, Nikolaos Ellinas, and Alexandros Potamianos. 2018 · 2018
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Ntua-slp at iest 2018: Ensemble of neural transfer methods for implicit emotion classification
Alexandra Chronopoulou, Aikaterini Margatina, Christos Baziotis, and Alexandros Potamianos. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Caiming Xiong, Victor Zhong, and Richard Socher. 2016 · 2016
Cited alongside, same era.
Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis
Christos Baziotis, Nikos Pelekis, and Christos Doulkeridis. 2017 · 2017
Cited alongside, same era.
Bb_twtr at semeval-2017 task 4: Twitter sentiment analysis with cnns and lstms
Mathieu Cliche. 2017 · 2017
Cited alongside, same era.
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Bjarke Felbo, Alan Mislove, Anders Sogaard, Iyad Rahwan, and Sune Lehmann. 2017 · 2017
Cited alongside, same era.
A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Cited alongside, same era.
Semi-supervised sequence tagging with bidirectional language models
Matthew Peters, Waleed Ammar, Chandra Bhagavatula, and Russell Power. 2017 · 2017
Cited alongside, same era.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Deep contextualized word representations for detecting sarcasm and irony
Suzana Ilic, Edison Marrese-Taylor, Jorge A. Balazs, and Yutaka Matsuo. 2018 · 2018
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Spell once, summon anywhere: A two-level open-vocabulary language model
Sebastian J. Mielke and Jason Eisner. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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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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Von mises-fisher loss for training sequence to sequence models with continuous outputs
Sachin Kumar and Yulia Tsvetkov. 2019 · 2019
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