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Natural Language Explanations for Machine Learning Classification Decisions

Burton, James; Al Moubayed, Noura; Enshaei, Amir

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Authors

James Burton james.burton@durham.ac.uk
PGR Student Doctor of Philosophy

Amir Enshaei



Contributors

Abstract

This paper addresses the challenge of providing understandable explanations for machine learning classification decisions. To do this, we introduce a dataset of expert-written textual explanations paired with numerical explanations, forming a data-to-text generation task. We fine-tune BART and T5 language models on this dataset to generate natural language explanations by linearizing the information represented by explainable output graphs. We find that the models can produce fluent and largely accurate textual explanations. We experiment with various configurations and see that an augmented dataset leads to a reduced error rate. Additionally, we probe the numerical explanations more directly by fine-tuning BART and T5 on a question-answer task and achieved an accuracy of 91% with T5.

Citation

Burton, J., Al Moubayed, N., & Enshaei, A. (2023). Natural Language Explanations for Machine Learning Classification Decisions. In 2023 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn54540.2023.10191637

Conference Name 2023 International Joint Conference on Neural Networks (IJCNN)
Conference Location Gold Coast, Australia
Start Date Jun 18, 2023
End Date Jun 23, 2023
Acceptance Date Jun 1, 2023
Online Publication Date Aug 2, 2023
Publication Date Jun 18, 2023
Deposit Date Aug 29, 2023
Publicly Available Date Aug 30, 2023
Series ISSN 2161-4393
Book Title 2023 International Joint Conference on Neural Networks (IJCNN)
ISBN 9781665488686
DOI https://doi.org/10.1109/ijcnn54540.2023.10191637
Public URL https://durham-repository.worktribe.com/output/1726329

Files

Accepted Conference Paper (1.1 Mb)
PDF

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