  Spoken language understanding (SLU) acts as a critical component in
goal-oriented dialog systems. It typically involves identifying the speakers
intent and extracting semantic slots from user utterances, which are known as
intent detection (ID) and slot filling (SF). SLU problem has been intensively
investigated in recent years. However, these methods just constrain SF results
grammatically, solve ID and SF independently, or do not fully utilize the
mutual impact of the two tasks. This paper proposes a multi-head self-attention
joint model with a conditional random field (CRF) layer and a prior mask. The
experiments show the effectiveness of our model, as compared with
state-of-the-art models. Meanwhile, online education in China has made great
progress in the last few years. But there are few intelligent educational
dialog applications for students to learn foreign languages. Hence, we design
an intelligent dialog robot equipped with different scenario settings to help
students learn communication skills.
