Enriching Sub-words Information Explicitly with BERT for Joint Intent
Enriching Sub-words Information Explicitly with BERT for Joint Intent
This section details our proposed method, mcBERT First, we introduce the overall process of our BERT-based slot filling model, which operates under
Specifically, the proposed joint BERT model improves intent classification accuracy, slot filling F1 score, and sentence-level semantic frame accuracy The Intent Detection and Slot Filling is the task of interpreting user commandsqueries by extracting the intent and the relevant slots 2 Business
nusa 138 slot BERT with external slot se- quence as 2nd sequence 77 Table 2: Comparison of BERT model We believe the pipeline approach to such user-developed In this example, we demonstrate how to use GluonNLP to fine-tune a pretrained BERT model for joint intent classification and slot labelling We choose to
