Natural Language Explanations of Classifier Behavior

被引：0

作者：

de Aquino, Rodrigo Monteiro ^{[1
]}

Cozman, Fabio Gagliardi ^{[1
]}

机构：

[1] Univ Sao Paulo, Escola Politecn, Sao Paulo, SP, Brazil

来源：

2019 IEEE SECOND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND KNOWLEDGE ENGINEERING (AIKE) | 2019年

关键词：

machine learning; interpretability; transparency;

D O I：

10.1109/AIKE.2019.00048

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Tools that enhance interpretability of classifiers tend to focus on the knowledgeable data scientist. Here we propose techniques that generate textual explanations of the internal behavior of a given classifier, aiming at less technically proficient users of machine learning resources. Our approach has been positively evaluated by a group of users who received its textual output.

引用

页码：239 / 242

页数：4

共 50 条

[21] Addressing Stability in Classifier Explanations
Samiei, Siavash
Baratalipour, Nasrin
Yadav, Pranjul
Roy, Amitabha
He, Dake
2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, : 1920 - 1927
[22] Tourism Recommender System Based on Natural Language Classifier
Tenemaza, Maritzol
Limaico, Jose
Lujan-Mora, Sergio
ADVANCES IN ARTIFICIAL INTELLIGENCE, SOFTWARE AND SYSTEMS ENGINEERING (AHFE 2021), 2021, 271 : 230 - 235
[23] SQLUCID: Grounding Natural Language Database Qeries with Interactive Explanations
Tian, Yuan
Kummerfeld, Jonathan K.
Li, Toby Jia-Jun
Zhang, Tianyi
PROCEEDINGS OF THE 37TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, USIT 2024, 2024,
[24] CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
Menon, Rakesh R.
Ghosh, Sayan
Srivastava, Shashank
PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS), 2022, : 6523 - 6546
[25] Generating Token-Level Explanations for Natural Language Inference
Thorne, James
Vlachos, Andreas
Christodoulopoulos, Christos
Mittal, Arpit
2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, 2019, : 963 - 969
[26] Towards Interpretable Natural Language Understanding with Explanations as Latent Variables
Zhou, Wangchunshu
Hu, Jinyi
Zhang, Hanlin
Liang, Xiaodan
Sun, Maosong
Xiong, Chenyan
Tang, Jian
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 33, NEURIPS 2020, 2020, 33
[27] Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction
Cui, Wanyun
Chen, Xingran
FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, ACL 2023, 2023, : 3454 - 3467
[28] Crowd-Based Personalized Natural Language Explanations for Recommendations
Chang, Shuo
Harper, F. Maxwell
Terveen, Loren
PROCEEDINGS OF THE 10TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'16), 2016, : 175 - 182
[29] INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations
Yu, Jialin
Cristea, Alexandra, I
Harit, Anoushka
Sun, Zhongtian
Aduragba, Olanrewaju Tahir
Shi, Lei
Al Moubayed, Noura
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2022,
[30] GazeXplain: Learning to Predict Natural Language Explanations of Visual Scanpaths
Chen, Xianyu
Jiang, Ming
Zhao, Qi
COMPUTER VISION - ECCV 2024, PT VIII, 2025, 15066 : 314 - 333

← 1 2 3 4 5 →