@article{Wickramanayake_Hsu_Lee_2019, title={FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/4100}, DOI={10.1609/aaai.v33i01.33012539}, abstractNote={<p>Explaining the decisions of a Deep Learning Network is imperative to safeguard end-user trust. Such explanations must be intuitive, descriptive, and faithfully explain why a model makes its decisions. In this work, we propose a framework called FLEX (Faithful Linguistic EXplanations) that generates post-hoc linguistic justifications to rationalize the decision of a Convolutional Neural Network. FLEX explains a model’s decision in terms of features that are responsible for the decision. We derive a novel way to associate such features to words, and introduce a new decision-relevance metric that measures the faithfulness of an explanation to a model’s reasoning. Experiment results on two benchmark datasets demonstrate that the proposed framework can generate discriminative and faithful explanations compared to state-of-the-art explanation generators. We also show how FLEX can generate explanations for images of unseen classes as well as automatically annotate objects in images.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Wickramanayake, Sandareka and Hsu, Wynne and Lee, Mong Li}, year={2019}, month={Jul.}, pages={2539-2546} }