  Utterance-level intent detection and token-level slot filling are two key
tasks for natural language understanding (NLU) in task-oriented systems. Most
existing approaches assume that only a single intent exists in an utterance.
However, there are often multiple intents within an utterance in real-life
scenarios. In this paper, we propose a multi-intent NLU framework, called SLIM,
to jointly learn multi-intent detection and slot filling based on BERT. To
fully exploit the existing annotation data and capture the interactions between
slots and intents, SLIM introduces an explicit slot-intent classifier to learn
the many-to-one mapping between slots and intents. Empirical results on three
public multi-intent datasets demonstrate (1) the superior performance of SLIM
compared to the current state-of-the-art for NLU with multiple intents and (2)
the benefits obtained from the slot-intent classifier.
