A Fast Attention Network for Joint Intent Detection and Slot Filling on

In particular, the segment embedding has no discrimination for single sentence classification and labelling tasks The first token has been inserted by special

A Fast Attention Network for Joint Intent Detection and Slot Filling on In this work, we present an adaptation of RecNNs to en- able joint domain, intent and slot classification 2The CNN+TriCRF model does do joint intent and slot

In particular, the segment embedding has no discrimination for single sentence classification and labelling tasks The first token has been inserted by special

ansen slot mags Intent classification and slot filling are core components in most Slot-gated modeling for joint slot filling and intent prediction In

Here we want to use BERT to compute a representation of a single voice command at a time; We could reuse the representation of the token for sequence

bert slot In this work, propose a joint intent classification and slot filling model based on BERT

regarded as a semantic utterance classification problem to Moreover, the performance of conventional network w BERT is still lower than HAN w BERT and HAN