Slot Canyon and Kangaroo Rats

BERT model to perform the classification over the slot labels for each word token A slot loss LS is computed using the output of the slot

Slot Canyon and Kangaroo Rats In this paper, we propose a multi-intent SLU framework, called SLIM, to jointly learn multi-intent detection and slot filling based on BERT To fully exploit

BERT model to perform the classification over the slot labels for each word token A slot loss LS is computed using the output of the slot

drottningholm slot In addition, we demonstrate that joint training as well as the use of pre-trained lan- guage models, ELMo and BERT in our case, are complementary to these few-

This work proposes a joint intent classification and slot filling model based on BERT that achieves significant improvement on intent classification

bert for joint intent classification and slot filling BERT model to perform the classification over the slot labels for each word token A slot loss LS is computed using the output of the slot

Experimental results show high accuracy on intent classification and slot filling tasks, allowing the robot to perform tasks accurately for a