  Intent detection and slot filling are two main tasks in natural language
understanding (NLU) for identifying users' needs from their utterances. These
two tasks are highly related and often trained jointly. However, most previous
works assume that each utterance only corresponds to one intent, ignoring the
fact that a user utterance in many cases could include multiple intents. In
this paper, we propose a novel Self-Distillation Joint NLU model (SDJN) for
multi-intent NLU. First, we formulate multiple intent detection as a weakly
supervised problem and approach with multiple instance learning (MIL). Then, we
design an auxiliary loop via self-distillation with three orderly arranged
decoders: Initial Slot Decoder, MIL Intent Decoder, and Final Slot Decoder. The
output of each decoder will serve as auxiliary information for the next
decoder. With the auxiliary knowledge provided by the MIL Intent Decoder, we
set Final Slot Decoder as the teacher model that imparts knowledge back to
Initial Slot Decoder to complete the loop. The auxiliary loop enables intents
and slots to guide mutually in-depth and further boost the overall NLU
performance. Experimental results on two public multi-intent datasets indicate
that our model achieves strong performance compared to others.
