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We present a novel rationale-centric framework with human-in-the-loop -- Rationales-centric Double-robustness Learning (RDL) -- to boost model out-of-distribution performance in few-shot learning scenarios.
Roberta: A robustly optimized bert pretraining approach
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Jinghui Lu and Brian MacNamee. 2020 · 2004
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Using “annotator rationales” to improve machine learning for text categorization
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
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Active learning literature survey
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Interacting meaningfully with machine learning systems: Three experiments
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Explanatory debugging: Supporting end-user debugging of machine-learned programs
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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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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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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
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Principles of explanatory debugging to personalize interactive machine learning
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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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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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith. 2018 · 2018
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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Towards hierarchical importance attribution: Explaining compositional semantics for neural sequence models
Xisen Jin, Zhongyu Wei, Junyi Du, Xiangyang Xue, and Xiang Ren. 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
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Explanatory interactive machine learning
Stefano Teso and Kristian Kersting. 2019 · 2019
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EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
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Learning the difference that makes a difference with counterfactually augmented data
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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Explanation-based human debugging of nlp models: A survey
Piyawat Lertvittayakumjorn and Francesca Toni. 2021 · 2021
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A sentence-level hierarchical bert model for document classification with limited labelled data
Jinghui Lu, Maeve Henchion, Ivan Bacher, and Brian Mac Namee. 2021 · 2021
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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
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Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton. 2020 · 2020
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Text and causal inference: A review of using text to remove confounding from causal estimates
Katherine Keith, David Jensen, and Brendan O’Connor. 2020 · 2020
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Find: Human-in-the-loop debugging deep text classifiers
Piyawat Lertvittayakumjorn, Lucia Specia, and Francesca Toni. 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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Robustness to spurious correlations via human annotations
Megha Srivastava, Tatsunori Hashimoto, and Percy Liang. 2020 · 2020
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An empirical study on robustness to spurious correlations using pre-trained language models
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Html: Hierarchical transformer-based multi-task learning for volatility prediction
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Counterfactual inference for text classification debiasing
Chen Qian, Fuli Feng, Lijie Wen, Chunping Ma, and Pengjun Xie. 2021 · 2021
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Right for better reasons: Training differentiable models by constraining their influence functions
Xiaoting Shao, Arseny Skryagin, Wolfgang Stammer, Patrick Schramowski, and Kristian Kersting. 2021 · 2021
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Right for the right concept: Revising neuro-symbolic concepts by interacting with their explanations
Wolfgang Stammer, Patrick Schramowski, and Kristian Kersting. 2021 · 2021
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Interactive label cleaning with example-based explanations
Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, and Andrea Passerini. 2021 · 2021
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Identifying and mitigating spurious correlations for improving robustness in nlp models
Tianlu Wang, Diyi Yang, and Xuezhi Wang. 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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A survey of human-in-the-loop for machine learning
Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, and Liang He. 2021 · 2021
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Exploring the efficacy of automatically generated counterfactuals for sentiment analysis
Linyi Yang, Jiazheng Li, Padraig Cunningham, Yue Zhang, Barry Smyth, and Ruihai Dong. 2021 · 2021
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Refining neural networks with compositional explanations
Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, and Xiang Ren. 2021 · 2021
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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