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Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations

7 October 2019

Papers citing "Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations"

23 / 73 papers shown

Title
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 178 422 0 20 Jan 2022
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup Yordan Yordanov Vid Kocijan Thomas Lukasiewicz Oana-Maria Camburu 87 19 0 12 Dec 2021
Tribrid: Stance Classification with Neural Inconsistency Detection Song Yang Jacopo Urbani 41 6 0 14 Sep 2021
Are Training Resources Insufficient? Predict First Then Explain! Myeongjun Jang Thomas Lukasiewicz LRM 59 7 0 29 Aug 2021
Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models Myeongjun Jang D. Kwon Thomas Lukasiewicz 71 13 0 15 Aug 2021
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations Bodhisattwa Prasad Majumder Oana-Maria Camburu Thomas Lukasiewicz Julian McAuley 96 36 0 25 Jun 2021
Prompting Contrastive Explanations for Commonsense Reasoning Tasks Bhargavi Paranjape Julian Michael Marjan Ghazvininejad Luke Zettlemoyer Hannaneh Hajishirzi ReLM LRM 76 68 0 12 Jun 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 138 142 0 17 May 2021
e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks Maxime Kayser Oana-Maria Camburu Leonard Salewski Cornelius Emde Virginie Do Zeynep Akata Thomas Lukasiewicz VLM 102 101 0 08 May 2021
Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards Marco Valentino Ian Pratt-Hartman André Freitas XAI LRM 80 11 0 05 May 2021
Explainability-aided Domain Generalization for Image Classification Robin M. Schmidt FAtt OOD 51 1 0 05 Apr 2021
Local Interpretations for Explainable Natural Language Processing: A Survey Siwen Luo Hamish Ivison S. Han Josiah Poon MILM 120 50 0 20 Mar 2021
Token-Modification Adversarial Attacks for Natural Language Processing: A Survey Tom Roth Yansong Gao A. Abuadbba Surya Nepal Wei Liu AAML 106 12 0 01 Mar 2021
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing Sarah Wiegreffe Ana Marasović XAI 78 146 0 24 Feb 2021
Measuring and Improving Consistency in Pretrained Language Models Yanai Elazar Nora Kassner Shauli Ravfogel Abhilasha Ravichander Eduard H. Hovy Hinrich Schütze Yoav Goldberg HILM 335 371 0 01 Feb 2021
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation Kushal Lakhotia Bhargavi Paranjape Asish Ghoshal Wen-tau Yih Yashar Mehdad Srini Iyer 63 28 0 31 Dec 2020
LIREx: Augmenting Language Inference with Relevant Explanation Xinyan Zhao V. Vydiswaran LRM 115 40 0 16 Dec 2020
Explaining Deep Neural Networks Oana-Maria Camburu XAI FAtt 108 26 0 04 Oct 2020
A Survey on Explainability in Machine Reading Comprehension Mokanarangan Thayaparan Marco Valentino André Freitas FaML 108 49 0 01 Oct 2020
QED: A Framework and Dataset for Explanations in Question Answering Matthew Lamm J. Palomaki Chris Alberti D. Andor Eunsol Choi Livio Baldini Soares Michael Collins 70 69 0 08 Sep 2020
NILE : Natural Language Inference with Faithful Natural Language Explanations Sawan Kumar Partha P. Talukdar XAI LRM 113 163 0 25 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 111 379 0 30 Apr 2020
Generating Natural Adversarial Examples Zhengli Zhao Dheeru Dua Sameer Singh GAN AAML 194 601 0 31 Oct 2017