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Spoken language understanding (SLU) acts as a critical component ingoal-oriented dialog systems. It typically involves identifying the speakersintent and extracting semantic slots from user utterances, which are known asintent detection (ID) and slot filling (SF). SLU problem has been intensivelyinvestigated in recent years. However, these methods just constrain SF resultsgrammatically, solve ID and SF independently, or do not fully utilize themutual impact of the two tasks. This paper proposes a multi-head self-attentionjoint model with a conditional random field (CRF) layer and a prior mask. Theexperiments show the effectiveness of our model, as compared withstate-of-the-art models. Meanwhile, online education in China has made greatprogress in the last few years. But there are few intelligent educationaldialog applications for students to learn foreign languages. Hence, we designan intelligent dialog robot equipped with different scenario settings to helpstudents learn communication skills.

A Self-Attention Joint Model for Spoken Language Understanding in Situational Dialog Applications