  Current researches on spoken language understanding (SLU) heavily are limited
to a simple setting: the plain text-based SLU that takes the user utterance as
input and generates its corresponding semantic frames (e.g., intent and slots).
Unfortunately, such a simple setting may fail to work in complex real-world
scenarios when an utterance is semantically ambiguous, which cannot be achieved
by the text-based SLU models. In this paper, we first introduce a new and
important task, Profile-based Spoken Language Understanding (ProSLU), which
requires the model that not only relies on the plain text but also the
supporting profile information to predict the correct intents and slots. To
this end, we further introduce a large-scale human-annotated Chinese dataset
with over 5K utterances and their corresponding supporting profile information
(Knowledge Graph (KG), User Profile (UP), Context Awareness (CA)). In addition,
we evaluate several state-of-the-art baseline models and explore a multi-level
knowledge adapter to effectively incorporate profile information. Experimental
results reveal that all existing text-based SLU models fail to work when the
utterances are semantically ambiguous and our proposed framework can
effectively fuse the supporting information for sentence-level intent detection
and token-level slot filling. Finally, we summarize key challenges and provide
new points for future directions, which hopes to facilitate the research.
