On guaranteed optimal robust explanations for NLP models
We build on abduction-based explanations for machine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP). Our explanations comprise a subset of the words of the input text that satisfies two key features: optimality w.r.t. a u...
Main Authors: | La Malfa, E, Michelmore, R, Zbrzezny, AM, Paoletti, N, Kwiatkowska, M |
---|---|
Format: | Conference item |
Language: | English |
Published: |
International Joint Conferences on Artificial Intelligence
2021
|
Similar Items
-
Statistical guarantees for the robustness of Bayesian neural networks
by: Cardelli, L, et al.
Published: (2019) -
Towards Faithful Model Explanation in NLP: A Survey
by: Qing Lyu, et al.
Published: (2024-07-01) -
Explanation-Based Human Debugging of NLP Models: A Survey
by: Piyawat Lertvittayakumjorn, et al.
Published: (2021-01-01) -
Robustness guarantees for deep neural networks on videos
by: Kwiatkowska, M, et al.
Published: (2020) -
Safety and robustness for deep learning with provable guarantees (keynote)
by: Kwiatkowska, M
Published: (2019)