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

Understand

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.