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While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data.
Roberta: A robustly optimized bert pretraining approach
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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Soo-Min Kim and Eduard Hovy. 2004 · 2004
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Learning what makes a difference from counterfactual examples and gradient supervision
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Framewise phoneme classification with bidirectional lstm and other neural network architectures
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Aligning faithful interpretations with their social attribution
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How much should i trust you? modeling uncertainty of black box explanations
Dylan Slack, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju. 2020b · 2008
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Dataset shift in machine learning
Joaquin Quionero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. 2009 · 2009
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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Sentiment analysis and opinion mining
Bing Liu. 2012 · 2012
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Machine learning in non-stationary environments: Introduction to covariate shift adaptation
Masashi Sugiyama and Motoaki Kawanabe. 2012 · 2012
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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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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
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Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio. 2017 · 2017
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Deep pyramid convolutional neural networks for text categorization
Rie Johnson and Tong Zhang. 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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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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The mythos of model interpretability
Zachary C Lipton. 2018 · 2018
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Sentence-state lstm for text representation
Yue Zhang, Qi Liu, and Linfeng Song. 2018 · 2018
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Counterfactuals in explainable artificial intelligence (xai): evidence from human reasoning
Ruth MJ Byrne. 2019 · 2019
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Counterfactual visual explanations
Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019 · 2019
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Towards hierarchical importance attribution: Explaining compositional semantics for neural sequence models
Evaluating models’ local decision boundaries via contrast sets
Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, et al. 2020 · 2020
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SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization
Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao. 2020 · 2020
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton. 2020 · 2020
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Good counterfactuals and where to find them: A case-based technique for generating counterfactuals for explainable ai (xai)
Mark T Keane and Barry Smyth. 2020 · 2020
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Maec: A multimodal aligned earnings conference call dataset for financial risk prediction
Jiazheng Li, Linyi Yang, Barry Smyth, and Ruihai Dong. 2020 · 2020
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Xisen Jin, Zhongyu Wei, Junyi Du, Xiangyang Xue, and Xiang Ren. 2019 · 2019
Cited alongside, same era.
It’s all in the name: Mitigating gender bias with name-based counterfactual data substitution
Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, and Simone Teufel. 2019 · 2019
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2019 · 2019
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019 · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek. 2019 · 2019
Cited alongside, same era.
Meaningful explanations of black box ai decision systems
Dino Pedreschi, Fosca Giannotti, Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, and Franco Turini. 2019 · 2019
Cited alongside, same era.
“transforming” delete, retrieve, generate approach for controlled text style transfer
Akhilesh Sudhakar, Bhargav Upadhyay, and Arjun Maheswaran. 2019 · 2019
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Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta. 2020 · 2020
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Politeness transfer: A tag and generate approach
Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, and Shrimai Prabhumoye. 2020 · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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Robustness to spurious correlations via human annotations
Megha Srivastava, Tatsunori Hashimoto, and Percy Liang. 2020 · 2020
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Blimp: The benchmark of linguistic minimal pairs for english
Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R Bowman. 2020 · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. 2020 · 2020
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Financial sentiment analysis: An investigation into common mistakes and silver bullets
Frank Xing, Lorenzo Malandri, Yue Zhang, and Erik Cambria. 2020 · 2020
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Html: Hierarchical transformer-based multi-task learning for volatility prediction
Linyi Yang, Tin Lok James Ng, Barry Smyth, and Riuhai Dong. 2020b · 2020
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SemEval-2020 task 5: Counterfactual recognition
Xiaoyu Yang, Stephen Obadinma, Huasha Zhao, Qiong Zhang, Stan Matwin, and Xiaodan Zhu. 2020c · 2020
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Word-level textual adversarial attacking as combinatorial optimization
Yuan Zang, Fanchao Qi, Chenghao Yang, Zhiyuan Liu, Meng Zhang, Qun Liu, and Maosong Sun. 2020 · 2020
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Explaining the efficacy of counterfactually augmented data
Divyansh Kaushik, Amrith Setlur, Eduard Hovy, and Zachary C Lipton. 2021 · 2021
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On generating plausible counterfactual and semi-factual explanations for deep learning
Eoin M Kenny and Mark T Keane. 2021 · 2021
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Robustness to spurious correlations in text classification via automatically generated counterfactuals
Zhao Wang and Aron Culotta. 2021 · 2021
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