Saliency Learning: Teaching the Model Where to Pay Attention

Saliency Learning: Teaching the Model Where to Pay Attention

22 February 2019

Prasad Tadepalli

Papers citing "Saliency Learning: Teaching the Model Where to Pay Attention"

11 / 11 papers shown

Title
On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs Nitay Calderon Roi Reichart 40 10 0 27 Jul 2024
Explanation Regularisation through the Lens of Attributions Pedro Ferreira Wilker Aziz Ivan Titov 43 1 0 23 Jul 2024
Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales Lucas Resck Marcos M. Raimundo Jorge Poco 47 1 0 03 Apr 2024
SCAAT: Improving Neural Network Interpretability via Saliency Constrained Adaptive Adversarial Training Rui Xu Wenkang Qin Peixiang Huang Hao Wang Lin Luo FAtt AAML 28 2 0 09 Nov 2023
Interpretability-Aware Vision Transformer Yao Qiang Chengyin Li Prashant Khanduri D. Zhu ViT 82 7 0 14 Sep 2023
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning Yuyang Gao Siyi Gu Junji Jiang S. Hong Dazhou Yu Liang Zhao 29 39 0 07 Dec 2022
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models Dong-Ho Lee Akshen Kadakia Brihi Joshi Aaron Chan Ziyi Liu ... Takashi Shibuya Ryosuke Mitani Toshiyuki Sekiya Jay Pujara Xiang Ren LRM 40 9 0 30 Oct 2022
SuMe: A Dataset Towards Summarizing Biomedical Mechanisms Mohaddeseh Bastan N. Shankar Mihai Surdeanu Niranjan Balasubramanian 21 3 0 10 May 2022
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo S. Feizi FAtt 20 80 0 29 Nov 2021
Diagnostics-Guided Explanation Generation Pepa Atanasova J. Simonsen Christina Lioma Isabelle Augenstein LRM FAtt 38 6 0 08 Sep 2021
Towards Robust Classification Model by Counterfactual and Invariant Data Generation C. Chang George Adam Anna Goldenberg OOD CML 21 31 0 02 Jun 2021