Explainable Slot Type Attentions to Improve Joint Intent Detection

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

Explainable Slot Type Attentions to Improve Joint Intent Detection We enrich a pre-trained language model, such as BERT or RoBERTa, to jointly solve the tasks of intent classification and slot filling • We introduce an

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

liga788 slot intent classification and slot-filling tasks on Indonesian and English datasets 1 Introduction The pretrained language models, such as

We implemented BERT for joint intent classification and slot filling on our dataset We also replaced the BERT backbone with other transformer-based models

bert for joint intent classification and slot filling Intent classification and slot filling are two core tasks in natural language understanding The interaction nature of the two tasks

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