Word-wise Explanation Method For Deep Learning Models Using Character N-gram Input

Estimating the contribution of input towards the prediction via model explainability methods can be insufficient to bring explainability to neural network models if the input consists of character n-grams. As characters can be within multiple words, the section where the prediction has obtained the most information from can not be localised within the text. In this study, novel methods to estimate the importance of words on the prediction of neural network models with character n-gram inputs have been proposed. Proposed methods have been tested on text obtained from an e-commerce platform and their performances have been quantitatively compared.