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Attention mechanisms in deep learning architectures have often been used as a means of transparency and, as such, to shed light on the inner workings of the architectures.
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Paul Michel, Omer Levy, and Graham Neubig. 2019 · 1905
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Sofia Serrano and Noah A Smith. 2019 · 1906
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Jesse Vig and Yonatan Belinkov. 2019 · 1906
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Do transformer attention heads provide transparency in abstractive summarization?
Joris Baan, Maartje ter Hoeve, Marlies van der Wees, Anne Schuth, and Maarten de Rijke. 2019 · 1907
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Adaptively sparse transformers
Gonçalo M Correia, Vlad Niculae, and André FT Martins. 2019 · 1909
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Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, and Manaal Faruqui. 2019 · 1909
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015 · 2015
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A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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Deep learning in neural networks: An overview
Jürgen Schmidhuber. 2015 · 2015
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Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016 · 2016
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From softmax to sparsemax: A sparse model of attention and multi-label classification
Andre Martins and Ramon Astudillo. 2016 · 2016
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche. 2017 · 2017
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Contextual string embeddings for sequence labeling
Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018 · 2018
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Latent alignment and variational attention
Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, and Alexander Rush. 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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Bottom-up abstractive summarization
Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018 · 2018
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Abstractive text summarization using sequence-to-sequence rnns and beyond
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Interpretable neural models for natural language processing
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Learning sparse neural networks through l _ 0 l\_0 regularization
Christos Louizos, Max Welling, and Diederik P Kingma. 2017 · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Reza Ghaeini, Xiaoli Z Fern, and Prasad Tadepalli. 2018 · 2018
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Explaining explanations: An approach to evaluating interpretability of machine learning
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Sparse and constrained attention for neural machine translation
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2018 · 2018
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Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018 · 2018
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Sparsemap: Differentiable sparse structured inference
Vlad Niculae, André FT Martins, Mathieu Blondel, and Claire Cardie. 2018 · 2018
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An analysis of encoder representations in transformer-based machine translation
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