  Joint intent detection and slot filling is a key research topic in natural
language understanding (NLU). Existing joint intent and slot filling systems
analyze and compute features collectively for all slot types, and importantly,
have no way to explain the slot filling model decisions. In this work, we
propose a novel approach that: (i) learns to generate additional slot type
specific features in order to improve accuracy and (ii) provides explanations
for slot filling decisions for the first time in a joint NLU model. We perform
an additional constrained supervision using a set of binary classifiers for the
slot type specific feature learning, thus ensuring appropriate attention
weights are learned in the process to explain slot filling decisions for
utterances. Our model is inherently explainable and does not need any post-hoc
processing. We evaluate our approach on two widely used datasets and show
accuracy improvements. Moreover, a detailed analysis is also provided for the
exclusive slot explainability.
