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Most evaluations of attribution methods focus on the English language.
Using the framework
The Fracas Consortium, Robin Cooper, Dick Crouch, Jan Van Eijck, Chris Fox, Josef Van Genabith, Jan Jaspars, Hans Kamp, David Milward, Manfred Pinkal, Massimo Poesio, Steve Pulman, Ted Briscoe, Holger Maier, and Karsten Konrad. 1996 · 1996
Earlier work this paper cites.
Entailment, intensionality and text understanding
Cleo Condoravdi, Dick Crouch, Valeria de Paiva, Reinhard Stolle, and Daniel G. Bobrow. 2003 · 2003
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Recognising textual entailment with logical inference
Johan Bos and Katja Markert. 2005 · 2005
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The pascal recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2005 · 2005
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An extended model of natural logic
Bill MacCartney and Christopher D. Manning. 2009 · 2009
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An efficient explanation of individual classifications using game theory
Erik Štrumbelj and Igor Kononenko. 2010 · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015 · 2015
Earlier work this paper cites.
A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015b · 2015
Earlier work this paper cites.
Visualizing and understanding recurrent networks
Andrej Karpathy, Justin Johnson, and Li Fei-Fei. 2015 · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
J.T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller. 2015 · 2015
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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
Earlier work this paper cites.
"what is relevant in a text document?": An interpretable machine learning approach
Leila Arras, F. Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
Earlier work this paper cites.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda B. Viégas, and Martin Wattenberg. 2017 · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Cited alongside, same era.
Local explanation methods for deep neural networks lack sensitivity to parameter values
Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, and Been Kim. 2018 · 2018
Cited alongside, same era.
e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Cited alongside, same era.
Xnli: Evaluating cross-lingual sentence representations
Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, and Veselin Stoyanov. 2018 · 2018
Cited alongside, same era.
Improving language understanding by generative pre-training
Alec Radford and Karthik Narasimhan. 2018 · 2018
Cited alongside, same era.
Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2020
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Interpretation of NLP models through input marginalization
Siwon Kim, Jihun Yi, Eunji Kim, and Sungroh Yoon. 2020 · 2020
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Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. 2020 · 2020
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Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020 · 2020
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A survey on recognizing textual entailment as an NLP evaluation
Adam Poliak. 2020 · 2020
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M. Robnik-Sikonja and Marko Bohanec. 2018 · 2018
Cited alongside, same era.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018a · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Attention is not explanation
Sarthak Jain and Byron C. Wallace. 2019 · 2019
Cited alongside, same era.
Similarity of neural network representations revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. 2019 · 2019
Cited alongside, same era.
Is attention interpretable?
Sofia Serrano and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
Berturk - bert models for turkish
Stefan Schweter. 2020 · 2020
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Interpreting predictions of nlp models
Eric Wallace, Matthew Thomas Gardner, and Sameer Singh. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
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Evaluating saliency methods for neural language models
Shuoyang Ding and Philipp Koehn. 2021 · 2021
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Word alignment by fine-tuning embeddings on parallel corpora
Zi-Yi Dou and Graham Neubig. 2021 · 2021
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Translation error detection as rationale extraction
M. Fomicheva, Lucia Specia, and Nikolaos Aletras. 2021 · 2021
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Relative importance in sentence processing
Nora Hollenstein and Lisa Beinborn. 2021 · 2021
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf. 2021 · 2021
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Andreas Madsen, Nicholas Meade, Vaibhav Adlakha, and Siva Reddy. 2021 · 2021
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To what extent do human explanations of model behavior align with actual model behavior?
Grusha Prasad, Yixin Nie, Mohit Bansal, Robin Jia, Douwe Kiela, and Adina Williams. 2021 · 2021
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Fine-grained interpretation and causation analysis in deep NLP models
Hassan Sajjad, Narine Kokhlikyan, Fahim Dalvi, and Nadir Durrani. 2021 · 2021
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Teach me to explain: A review of datasets for explainable nlp
Sarah Wiegreffe and Ana Marasović. 2021 · 2021
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