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Pretrained transformer-based models such as BERT have demonstrated state-of-the-art predictive performance when adapted into a range of natural language processing tasks.
Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Attention interpretability across NLP tasks
Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar, and Manaal Faruqui. 2019 · 1909
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Do attention heads in bert track syntactic dependencies?
Phu Mon Htut, Jason Phang, Shikha Bordia, and Samuel R Bowman. 2019 · 1911
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TextRank: Bringing order into text
Rada Mihalcea and Paul Tarau. 2004 · 2004
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Ranking a stream of news
Gianna M Del Corso, Antonio Gulli, and Francesco Romani. 2005 · 2005
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Explaining classifications for individual instances
Marko Robnik-Šikonja and Igor Kononenko. 2008 · 2008
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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The role of adverbs in sentiment analysis
Eduard Dragut and Christiane Fellbaum. 2014 · 2014
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Adjective intensity and sentiment analysis
Raksha Sharma, Mohit Gupta, Astha Agarwal, and Pushpak Bhattacharyya. 2015 · 2015
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Explaining predictions of non-linear classifiers in NLP
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2016 · 2016
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Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne. 2016 · 2016
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Visualizing and understanding neural models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016a · 2016
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“why should I trust you?”: Explaining the predictions of any classifier
Marco Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Explaining recurrent neural network predictions in sentiment analysis
Leila Arras, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 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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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
Cited alongside, same era.
Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen. 2018a · 2018
Cited alongside, same era.
Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen. 2018b · 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 Bowman. 2018 · 2018
Cited alongside, same era.
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
Jasmijn Bastings and Katja Filippova. 2020 · 2020
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ERASER: A benchmark to evaluate rationalized NLP models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2020 · 2020
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Why attention is not explanation: Surgical intervention and causal reasoning about neural models
Christopher Grimsley, Elijah Mayfield, and Julia R.S. Bursten. 2020 · 2020
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spaCy: Industrial-strength Natural Language Processing in Python
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020 · 2020
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Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg. 2020 · 2020
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Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Cited alongside, same era.
On identifiability in transformers
Gino Brunner, Yang Liu, Damian Pascual, Oliver Richter, Massimiliano Ciaramita, and Roger Wattenhofer. 2019 · 2019
Cited alongside, same era.
What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
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.
Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Zou. 2019 · 2019
Cited alongside, same era.
Inferring which medical treatments work from reports of clinical trials
Eric Lehman, Jay DeYoung, Regina Barzilay, and Byron C. Wallace. 2019 · 2019
Cited alongside, same era.
Incorporating priors with feature attribution on text classification
Frederick Liu and Besim Avci. 2019 · 2019
Cited alongside, same era.
Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C. Wallace. 2020 · 2020
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Contextualizing hate speech classifiers with post-hoc explanation
Brendan Kennedy, Xisen Jin, Aida Mostafazadeh Davani, Morteza Dehghani, and Xiang Ren. 2020 · 2020
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Towards transparent and explainable attention models
Akash Kumar Mohankumar, Preksha Nema, Sharan Narasimhan, Mitesh M. Khapra, Balaji Vasan Srinivasan, and Balaraman Ravindran. 2020 · 2020
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Learning to deceive with attention-based explanations
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, and Zachary C. Lipton. 2020 · 2020
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The explanation game: Towards prediction explainability through sparse communication
Marcos Treviso and André F. T. Martins. 2020 · 2020
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Staying true to your word: (how) can attention become explanation?
Martin Tutek and Jan Snajder. 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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Self-attention guided copy mechanism for abstractive summarization
Song Xu, Haoran Li, Peng Yuan, Youzheng Wu, Xiaodong He, and Bowen Zhou. 2020 · 2020
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Improving the faithfulness of attention-based explanations with task-specific information for text classification
George Chrysostomou and Nikolaos Aletras. 2021 · 2021
Closest in time.
Measuring and improving faithfulness of attention in neural machine translation
Pooya Moradi, Nishant Kambhatla, and Anoop Sarkar. 2021 · 2021
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Telling BERT’s full story: from local attention to global aggregation
Damian Pascual, Gino Brunner, and Roger Wattenhofer. 2021 · 2021
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