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We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets.
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Learning Gaussian processes from multiple tasks
Kai Yu, Volker Tresp, and Anton Schwaighofer. 2005 · 2005
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Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
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Multi-Task Learning for Classification with Dirichlet Process Priors
Ya Xue, Xuejun Liao, Lawrence Carin, and Balaji Krishnapuram. 2007 · 2007
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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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Bayesian multitask learning with latent hierarchies
Hal Daumé III. 2009 · 2009
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Clustered Multi-Task Learning: A Convex Formulation
Laurent Jacob, Jean-Philippe Vert, Francis R Bach, and Jean-philippe Vert. 2009 · 2009
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Natural language processing (almost) from scratch
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Learning with Whom to Share in Multi-task Feature Learning
Zhuoliang Kang, Kristen Grauman, and Fei Sha. 2011 · 2011
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Learning Task Grouping and Overlap in Multi-task Learning
Abhishek Kumar and Hal Daumé III. 2012 · 2012
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Learning to Map into a Universal POS Tagset
Yuan Zhang, Roi Reichart, Regina Barzilay, and Amir Globerson. 2012 · 2012
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Relation Extraction with Matrix Factorization and Universal Schemas
Sebastian Riedel, Limin Yao, Andrew McCallum, and Benjamin M. Marlin. 2013 · 2013
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Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification
Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014 · 2014
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Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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New Transfer Learning Techniques for Disparate Label Sets
Young-Bum Kim, Karl Stratos, Ruhi Sarikaya, and Minwoo Jeong. 2015 · 2015
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Target-Dependent Twitter Sentiment Classification with Rich Automatic Features
Duy-Tin Vo and Yue Zhang. 2015 · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. 2016 · 2016
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Twitter Stance Detection with Bidirectional Conditional Encoding
Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. 2016 · 2016
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TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Georgios Balikas and Massih-Reza Amini. 2016 · 2016
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Deep multi-task learning with low level tasks supervised at lower layers
Anders Søgaard and Yoav Goldberg. 2016 · 2016
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Multi-task learning of keyphrase boundary detection
Isabelle Augenstein and Anders Søgaard. 2017 · 2017
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Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Bingel and Anders Søgaard. 2017 · 2017
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Will my auxiliary tagging task help? Estimating Auxiliary Tasks Effectivity in Multi-Task Learning
Johannes Bjerva. 2017 · 2017
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Learning attention for historical text normalization by learning to pronounce
Marcel Bollman, Joachim Bingel, and Anders Søgaard. 2017 · 2017
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Recurrent neural network-based sentence encoder with gated attention for natural language inference
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XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modelling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis
Caroline Brun, Julien Perez, and Claude Roux. 2016 · 2016
Cited alongside, same era.
Neural Network for Heterogeneous Annotations
Hongshen Chen, Yue Zhang, and Qun Liu. 2016 · 2016
Cited alongside, same era.
emoji2vec: Learning Emoji Representations from their Description
Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, Matko Bosnjak, and Sebastian Riedel. 2016 · 2016
Cited alongside, same era.
IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis
Ayush Kumar, Sarah Kohail, Amit Kumar, Asif Ekbal, and Chris Biemann. 2016 · 2016
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Multi-task Sequence to Sequence Learning
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 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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SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Veselin Stoyanov, and Fabrizio Sebastiani. 2016 · 2016
Cited alongside, same era.
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 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
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A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017 · 2017
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Adversarial Multi-task Learning for Text Classification
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2017 · 2017
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The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
Nikita Nangia, Adina Williams, Angeliki Lazaridou, and Samuel R. Bowman. 2017 · 2017
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Deep Multitask Learning for Semantic Dependency Parsing
Hao Peng, Sam Thomson, Noah A Smith, and Paul G Allen. 2017 · 2017
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Semi-supervised Multitask Learning for Sequence Labeling
Marek Rei. 2017 · 2017
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A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Benjamin Riedel, Isabelle Augenstein, Georgios P Spithourakis, and Sebastian Riedel. 2017 · 2017
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Sluice networks: Learning what to share between loosely related tasks
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2017 · 2017
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Learning to select data for transfer learning with Bayesian Optimization
Sebastian Ruder and Barbara Plank. 2017 · 2017
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Learning Deep Latent Space for Multi-Label Classification
Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko, and Yu-Chiang Frank Wang. 2017 · 2017
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