Fetching the paper…
Reading the bibliography…
While deep learning models have greatly improved the performance of most artificial intelligence tasks, they are often criticized to be untrustworthy due to the black-box problem.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Deep learning: methods and applications
Li Deng and Dong Yu · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Yes, we care! results of the ethics and natural language processing surveys
Karën Fort and Alain Couillault · 2016
Earlier work this paper cites.
" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
Earlier work this paper cites.
A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
Earlier work this paper cites.
Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
Earlier work this paper cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Earlier work this paper cites.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Earlier work this paper cites.
Towards linguistically generalizable nlp systems: A workshop and shared task
Allyson Ettinger, Sudha Rao, Hal Daumé III, and Emily M Bender · 2017
Earlier work this paper cites.
The promise and peril of human evaluation for model interpretability
Bernease Herman · 2017
Cited alongside, same era.
A survey on deep learning: Algorithms, techniques, and applications
Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and Sundaraja S Iyengar · 2018
Cited alongside, same era.
Generating natural language adversarial examples
Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang · 2018
Cited alongside, same era.
Breaking nli systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg · 2018
Cited alongside, same era.
Analysis methods in neural language processing: A survey
Yonatan Belinkov and James Glass · 2019
Cited alongside, same era.
Why deep-learning ais are so easy to fool, 2019
Ernie: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu · 2019
Later among the works it cites.
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
Later among the works it cites.
Beyond accuracy: Behavioral testing of nlp models with checklist
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh · 2020
Later among the works it cites.
A survey on neural network interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis, and Ke Tang · 2020
Later among the works it cites.
Learning to faithfully rationalize by construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, and Byron C Wallace · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Douglas Heaven · 2019
Cited alongside, same era.
Models in the wild: On corruption robustness of neural nlp systems
Barbara Rychalska, Dominika Basaj, Alicja Gosiewska, and Przemysław Biecek · 2019
Cited alongside, same era.
Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen · 2019
Cited alongside, same era.
Attention is not explanation
Sarthak Jain and Byron C Wallace · 2019
Cited alongside, same era.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
Cited alongside, same era.
Towards a robust deep neural network in texts: A survey
Wenqi Wang, Run Wang, Lina Wang, Zhibo Wang, and Aoshuang Ye · 2019
Cited alongside, same era.
Towards faithfully interpretable nlp systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
Later among the works it cites.
Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2020
Later among the works it cites.
Learning to deceive with attention-based explanations
Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, Graham Neubig, and Zachary C Lipton · 2020
Later among the works it cites.
Skep: Sentiment knowledge enhanced pre-training for sentiment analysis
Hao Tian, Can Gao, Xinyan Xiao, Hao Liu, Bolei He, Hua Wu, Haifeng Wang, et al · 2020
Later among the works it cites.
Textflint: Unified multilingual robustness evaluation toolkit for natural language processing
Tao Gui, Xiao Wang, Qi Zhang, Qin Liu, Yicheng Zou, Xin Zhou, Rui Zheng, Chong Zhang, Qinzhuo Wu, Jiacheng Ye, et al · 2021
Closest in time.
Evaluating saliency methods for neural language models
Shuoyang Ding and Philipp Koehn · 2021
Closest in time.