A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detection

Understand

The success of interactive dialog systems is usually associated with the quality of the spoken language understanding (SLU) task, which mainly identifies the corresponding dialog acts and slot values in each turn.

• By treating utterances in isolation, most SLU systems often overlook the semantic context in which a dialog act is expected.
• The act dependency between turns is non-trivial and yet critical to the identification of the correct semantic representations.
• Previous works with limited context awareness have exposed the inadequacy of dealing with complexity in multiproned user intents, which are subject to spontaneous change during turn transitions.