MARTA: Leveraging Human Rationales for Explainable Text Classification (original) (raw)
Related papers
Explainable question answering beyond F1: metrics, models and human evaluation
2020
arXiv (Cornell University), 2022
Rationale-Inspired Natural Language Explanations with Commonsense
2021
Deep NLP Explainer: Using Prediction Slope to Explain NLP Models
Lecture Notes in Computer Science, 2021
Explainable Machine Learning with Prior Knowledge: An Overview
ArXiv, 2021
ArXiv, 2021
Towards Learning with Feature-Based Explanations for Document Classification
2016
Xplique: A Deep Learning Explainability Toolbox
Cornell University - arXiv, 2022
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features
ArXiv, 2021
Explainable AI for NLP: Decoding Black Box
International Journal of Computer Trends and Technology, 2022
Teaching the Machine to Explain Itself using Domain Knowledge
ArXiv, 2020
Explaining Explanations: An Overview of Interpretability of Machine Learning
2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)
2023
Explainable Artificial Intelligence in Machine Learning
Capstone Project Report, 2024
Challenges and Opportunities in Text Generation Explainability
arXiv (Cornell University), 2024
A Comprehensive Review on Explainable AI Techniques, Challenges, and Future Scope
Intelligent Computing and Networking
Levels of explainable artificial intelligence for human-aligned conversational explanations
Artificial Intelligence, 2021
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
Communications in Computer and Information Science, 2021
Using ``Annotator Rationales'' to Improve Machine Learning for Text Categorization
2007
Toward Practical Usage of the Attention Mechanism as a Tool for Interpretability
IEEE Access
On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations
IFIP advances in information and communication technology, 2022
Trusting deep learning natural-language models via local and global explanations
Knowledge and Information Systems
An Experimental Investigation into the Evaluation of Explainability Methods
arXiv (Cornell University), 2023
Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN
ArXiv, 2018
Pitfalls of Explainable ML: An Industry Perspective
ArXiv, 2021
Explainable Artificial Intelligence and Machine Learning
Computer
The Role of Human Knowledge in Explainable AI
Data
Information-seeking dialogue for explainable artificial intelligence: Modelling and analytics
Argument & Computation
Robust Explainability: A Tutorial on Gradient-Based Attribution Methods for Deep Neural Networks
ArXiv, 2021
Argflow: A Toolkit for Deep Argumentative Explanations for Neural Networks
2021
On the Explainability of Natural Language Processing Deep Models
ACM Computing Surveys
AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models
2020
Knowledge-Intensive Language Understanding for Explainable AI
IEEE Internet Computing