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Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Yoon Kim. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Lei Ba. 2014 · 2014
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The stanford corenlp natural language processing toolkit
Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Recurrent convolutional neural networks for text classification
Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. 2015 · 2015
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015 · 2015
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Document modeling with gated recurrent neural network for sentiment classification
Duyu Tang, Bing Qin, and Ting Liu. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Neural headline generation with minimum risk training
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J Gu, Z Lu, H Li, and VOK Li. 2016 · 2016
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Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Recurrent neural network for text classification with multi-task learning
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016 · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Training classifiers with natural language explanations
Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, and Christopher Ré. 2018 · 2018
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Stack-pointer networks for dependency parsing
Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig, and Eduard Hovy. 2018 · 2018
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Improving explainable recommendations with synthetic reviews
Sixun Ouyang, Aonghus Lawlor, Felipe Costa, and Peter Dolog. 2018 · 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 · 2018
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Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models
Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller. 2018 · 2018
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