Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding
TABLE VII: Slot F1 scores of different models with and without CRF layers on AICar dataset, when FLIGHT is the target domain and the rest two are the source domains. 10% training data of FLIGHT is used for fine-tuning, and results are averaged over 10 random data splits.
['[BOLD] Method', 'w/o CRF', 'with CRF  [ITALIC] scalar', 'with CRF  [ITALIC] bilinear']
We want to know whether label embeddings can improve different slot filling models. From the table, we can see that CRF layers give improvements for the baseline and our methods. The bilinear model calculates the transition score of two labels depending on their label embeddings, which shares transition patterns of related labels. It helps mitigate the data sparsity problem.