  Recent joint intent detection and slot tagging models have seen improved
performance when compared to individual models. In many real-world datasets,
the slot labels and values have a strong correlation with their intent labels.
In such cases, the intent label information may act as a useful feature to the
slot tagging model. In this paper, we examine the effect of leveraging intent
label features through 3 techniques in the slot tagging task of joint intent
and slot detection models. We evaluate our techniques on benchmark spoken
language datasets SNIPS and ATIS, as well as over a large private Bixby dataset
and observe an improved slot-tagging performance over state-of-the-art models.
