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1707.01943
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
6 July 2017
David Alvarez-Melis
Tommi Jaakkola
CML
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Papers citing
"A causal framework for explaining the predictions of black-box sequence-to-sequence models"
41 / 41 papers shown
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Towards Explainable AI Writing Assistants for Non-native English Speakers
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Understanding and Detecting Hallucinations in Neural Machine Translation via Model Introspection
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Influence Functions for Sequence Tagging Models
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Varun Manjunatha
Byron C. Wallace
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25 Oct 2022
On the Explainability of Natural Language Processing Deep Models
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An Interpretability Evaluation Benchmark for Pre-trained Language Models
Ya-Ming Shen
Lijie Wang
Ying Chen
Xinyan Xiao
Jing Liu
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Leveraging Causal Inference for Explainable Automatic Program Repair
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Shijing Si
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Zhenhou Hong
Jing Xiao
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Can Rationalization Improve Robustness?
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Jacqueline He
Karthik Narasimhan
Danqi Chen
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25 Apr 2022
VALUE: Understanding Dialect Disparity in NLU
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Jiaao Chen
Camille Harris
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Diyi Yang
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Discovering Invariant Rationales for Graph Neural Networks
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Xiang Wang
An Zhang
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30 Jan 2022
Matching Learned Causal Effects of Neural Networks with Domain Priors
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Let the CAT out of the bag: Contrastive Attributed explanations for Text
Saneem A. Chemmengath
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26
10
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16 Sep 2021
Counterfactual Evaluation for Explainable AI
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Shuchang Liu
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Shuyuan Xu
Shijie Geng
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Towards Out-Of-Distribution Generalization: A Survey
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Zheyan Shen
Yue He
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Peng Cui
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Rationalization through Concepts
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Boi Faltings
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Local Interpretations for Explainable Natural Language Processing: A Survey
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Hamish Ivison
S. Han
Josiah Poon
MILM
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20 Mar 2021
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
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Xuezhi Wang
Yao Qin
Ben Packer
Kang Li
Jilin Chen
Alex Beutel
Ed H. Chi
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Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control
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Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
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Generative causal explanations of black-box classifiers
Matthew R. O’Shaughnessy
Gregory H. Canal
Marissa Connor
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Christopher Rozell
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73
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24 Jun 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
Raha Moraffah
Mansooreh Karami
Ruocheng Guo
A. Raglin
Huan Liu
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212
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09 Mar 2020
Weight of Evidence as a Basis for Human-Oriented Explanations
David Alvarez-Melis
Hal Daumé
Jennifer Wortman Vaughan
Hanna M. Wallach
XAI
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11
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29 Oct 2019
A Game Theoretic Approach to Class-wise Selective Rationalization
Shiyu Chang
Yang Zhang
Mo Yu
Tommi Jaakkola
15
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28 Oct 2019
MonoNet: Towards Interpretable Models by Learning Monotonic Features
An-phi Nguyen
María Rodríguez Martínez
FAtt
8
13
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30 Sep 2019
On Model Stability as a Function of Random Seed
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Dhruv Batra
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Evaluating Gender Bias in Machine Translation
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Noah A. Smith
Luke Zettlemoyer
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Interpretable Neural Predictions with Differentiable Binary Variables
Jasmijn Bastings
Wilker Aziz
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211
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20 May 2019
Semantics Preserving Adversarial Learning
Ousmane Amadou Dia
Elnaz Barshan
Reza Babanezhad
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GAN
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2
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10 Mar 2019
e-SNLI: Natural Language Inference with Natural Language Explanations
Oana-Maria Camburu
Tim Rocktaschel
Thomas Lukasiewicz
Phil Blunsom
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255
620
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An Operation Sequence Model for Explainable Neural Machine Translation
Felix Stahlberg
Danielle Saunders
Bill Byrne
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25
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29 Aug 2018
On the Robustness of Interpretability Methods
David Alvarez-Melis
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521
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Towards Robust Interpretability with Self-Explaining Neural Networks
David Alvarez-Melis
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13
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AGI Safety Literature Review
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115
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Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models
Hendrik Strobelt
Sebastian Gehrmann
M. Behrisch
Adam Perer
Hanspeter Pfister
Alexander M. Rush
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23
239
0
25 Apr 2018
Generating Natural Adversarial Examples
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Dheeru Dua
Sameer Singh
GAN
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596
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OpenNMT: Open-Source Toolkit for Neural Machine Translation
Guillaume Klein
Yoon Kim
Yuntian Deng
Jean Senellart
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256
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0
10 Jan 2017
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