Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

20 December 2021

Baotian Hu

Papers citing "Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction"

23 / 23 papers shown

Title
Masked Conditional Random Fields for Sequence Labeling Tianwen Wei Jianwei Qi Shenghuang He Songtao Sun 37 18 0 19 Mar 2021
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration Lei Sha Oana-Maria Camburu Thomas Lukasiewicz 162 37 0 16 Dec 2020
Weakly- and Semi-supervised Evidence Extraction Danish Pruthi Bhuwan Dhingra Graham Neubig Zachary Chase Lipton 54 23 0 03 Nov 2020
Posterior Differential Regularization with f-divergence for Improving Model Robustness Hao Cheng Xiaodong Liu L. Pereira Yaoliang Yu Jianfeng Gao 265 31 0 23 Oct 2020
Evaluating and Characterizing Human Rationales Samuel Carton Anirudh Rathore Chenhao Tan 53 49 0 09 Oct 2020
Beyond Accuracy: Behavioral Testing of NLP models with CheckList Marco Tulio Ribeiro Tongshuang Wu Carlos Guestrin Sameer Singh ELM 208 1,103 0 08 May 2020
An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction Bhargavi Paranjape Mandar Joshi John Thickstun Hannaneh Hajishirzi Luke Zettlemoyer 57 101 0 01 May 2020
Learning to Faithfully Rationalize by Construction Sarthak Jain Sarah Wiegreffe Yuval Pinter Byron C. Wallace 75 163 0 30 Apr 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 154 102 0 20 Mar 2020
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 T. Zhao 86 561 0 08 Nov 2019
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment Di Jin Zhijing Jin Qiufeng Wang Peter Szolovits SILM AAML 174 1,077 0 27 Jul 2019
On the Connection Between Adversarial Robustness and Saliency Map Interpretability Christian Etmann Sebastian Lunz Peter Maass Carola-Bibiane Schönlieb AAML FAtt 58 161 0 10 May 2019
Inferring Which Medical Treatments Work from Reports of Clinical Trials Eric P. Lehman Jay DeYoung Regina Barzilay Byron C. Wallace 87 116 0 02 Apr 2019
On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models Paul Michel Xian Li Graham Neubig J. Pino AAML 65 136 0 15 Mar 2019
TextBugger: Generating Adversarial Text Against Real-world Applications Jinfeng Li S. Ji Tianyu Du Bo Li Ting Wang SILM AAML 208 738 0 13 Dec 2018
e-SNLI: Natural Language Inference with Natural Language Explanations Oana-Maria Camburu Tim Rocktaschel Thomas Lukasiewicz Phil Blunsom LRM 408 637 0 04 Dec 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 76 526 0 21 Jun 2018
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models Wojciech Samek Thomas Wiegand K. Müller XAI VLM 72 1,189 0 28 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 304 12,063 0 19 Jun 2017
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations A. Ross M. C. Hughes Finale Doshi-Velez FAtt 120 589 0 10 Mar 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,699 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 274 19,049 0 20 Dec 2014