  In large-scale domain classification for natural language understanding,
leveraging each user's domain enablement information, which refers to the
preferred or authenticated domains by the user, with attention mechanism has
been shown to improve the overall domain classification performance. In this
paper, we propose a supervised enablement attention mechanism, which utilizes
sigmoid activation for the attention weighting so that the attention can be
computed with more expressive power without the weight sum constraint of
softmax attention. The attention weights are explicitly encouraged to be
similar to the corresponding elements of the ground-truth's one-hot vector by
supervised attention, and the attention information of the other enabled
domains is leveraged through self-distillation. By evaluating on the actual
utterances from a large-scale IPDA, we show that our approach significantly
improves domain classification performance.
