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Evaluating the Faithfulness of Importance Measures in NLP by Recursively
  Masking Allegedly Important Tokens and Retraining

Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining

15 October 2021
Andreas Madsen
Nicholas Meade
Vaibhav Adlakha
Siva Reddy
ArXivPDFHTML

Papers citing "Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining"

3 / 3 papers shown
Title
Human Interpretation of Saliency-based Explanation Over Text
Human Interpretation of Saliency-based Explanation Over Text
Hendrik Schuff
Alon Jacovi
Heike Adel
Yoav Goldberg
Ngoc Thang Vu
MILM
XAI
FAtt
128
31
0
27 Jan 2022
"Will You Find These Shortcuts?" A Protocol for Evaluating the
  Faithfulness of Input Salience Methods for Text Classification
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification
Jasmijn Bastings
Sebastian Ebert
Polina Zablotskaia
Anders Sandholm
Katja Filippova
92
61
0
14 Nov 2021
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
219
2,098
0
28 Feb 2017
1