Semantic Slot Filling Based on BERT and BiLSTM

The model employs pre-trained BERT to extract semantic features and utilizes semantic fusion to associate and integrate this information The

While non-categorical slots are classified by detecting relevant spans in the dialogue, categorical slots use a fixed BERT model to encode all

bert for joint intent classification and slot filling For intent detection and slot filling, the use of BERT has also brought about a significant effect improvement in joint models However, although

tree of gold slot slot filling based on BERT To fully exploit the existing annotation data and capture the interactions between slots and intents, SLIM introduces an explicit