Justifying convolutional neural network with argumentation for explainability.

Convolutional neural network (CNN) has emerged as one of the most accurate methods for sentiment analysis, but it is largely uninterpretable, while case-based reasoning (CBR) is less accurate but o ff ers interpretable outputs in the form of arguments from analogy. This paper presents an approach to combine these two methods, CNN for accuracy and CBR for explainability, using an assumption-based argumentation (ABA) framework. Our approach focuses on justifying CNN outputs using analogous sentences from CBR, while ensuring that the combined process is argumentative and hence self-explainable. To demonstrate the proposal, we construct a CNN model M 1 and a CBR model M 2 for sentiment analysis using di ff erent subsets of a dataset of which the remaining part is used for testing and comparing these input models with combined models. For an input sentence, if M 1 and M 2 predict the same sentiment, then the analogous sentence, which M 2 finds, is used to explain the sentiment. If they give conflicting sentiments, a hybrid model M 3 determines which one should be followed using a system of strict rules that takes into account how assertive M 1 and M 2 are. Another hybrid model M 4 , which is implemented by an ABA framework, improves on M 3 by considering the probability distribution of the set of all labels from M 1 , and the second (or third) most similar sentences from M 2 . M 3 and M 4 preserve the accuracy of the CNN model (specifically, 88.32% and 88.28% in comparison with 87.59% accuracy of the CNN). They justify 69.95% and 74.53% of CNN outputs, respectively.