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Joint intent detection and slot filling is a key research topic in naturallanguage understanding (NLU). Existing joint intent and slot filling systemsanalyze and compute features collectively for all slot types, and importantly,have no way to explain the slot filling model decisions. In this work, wepropose a novel approach that: (i) learns to generate additional slot typespecific features in order to improve accuracy and (ii) provides explanationsfor slot filling decisions for the first time in a joint NLU model. We performan additional constrained supervision using a set of binary classifiers for theslot type specific feature learning, thus ensuring appropriate attentionweights are learned in the process to explain slot filling decisions forutterances. Our model is inherently explainable and does not need any post-hocprocessing. We evaluate our approach on two widely used datasets and showaccuracy improvements. Moreover, a detailed analysis is also provided for theexclusive slot explainability.

Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling