Modified Transformer Architecture to Explain Black Box Models in Narrative Form

Abstract

The current XAI techniques present explanations mainly as visuals and structured data. However, these explanations are difficult to interpret for a non-expert user. Here, the use of natural language generation (NLG)-based techniques can help to represent explanations in a human-understandable format. The paper addresses the issue of automatic generation of narratives using a modified transformer approach. Further, due to the unavailability of a relevant annotated dataset for development and testing, the authors also propose a verbalization template approach to generate the same. The input of the transformer is linearized to convert the data-to-text task into text-to-text task. The proposed work is evaluated on a verbalized explained PIMA Indians diabetes dataset and exhibits significant improvement as compared to existing baselines for both manual and automatic evaluation. Also, the narratives provide better comprehensibility to be trusted by human evaluators than the non-NLG counterparts. Lastly, an ablation study is performed in order to understand the contribution of each component.