  Joint intent detection and slot filling, which is also termed as joint NLU
(Natural Language Understanding) is invaluable for smart voice assistants.
Recent advancements in this area have been heavily focusing on improving
accuracy using various techniques. Explainability is undoubtedly an important
aspect for deep learning-based models including joint NLU models. Without
explainability, their decisions are opaque to the outside world and hence, have
tendency to lack user trust. Therefore to bridge this gap, we transform the
full joint NLU model to be `inherently' explainable at granular levels without
compromising on accuracy. Further, as we enable the full joint NLU model
explainable, we show that our extension can be successfully used in other
general classification tasks. We demonstrate this using sentiment analysis and
named entity recognition.
