  Interpretability and explainability of deep neural networks are challenging
due to their scale, complexity, and the agreeable notions on which the
explaining process rests. Previous work, in particular, has focused on
representing internal components of neural networks through human-friendly
visuals and concepts. On the other hand, in real life, when making a decision,
human tends to rely on similar situations and/or associations in the past.
Hence arguably, a promising approach to make the model transparent is to design
it in a way such that the model explicitly connects the current sample with the
seen ones, and bases its decision on these samples. Grounded on that principle,
we propose in this paper an explainable, evidence-based memory network
architecture, which learns to summarize the dataset and extract supporting
evidences to make its decision. Our model achieves state-of-the-art performance
on two popular question answering datasets (i.e. TrecQA and WikiQA). Via
further analysis, we show that this model can reliably trace the errors it has
made in the validation step to the training instances that might have caused
these errors. We believe that this error-tracing capability provides
significant benefit in improving dataset quality in many applications.
