Prior Knowledge Driven Label Embedding for Slot Filling in Natural Language Understanding
TABLE IV: Slot F1 scores on DSTC3 test set with or without the training data of DSTC2. The bold number indicates a statistically significant gain over the best baseline.
['[BOLD] Method', 'DSTC3_train (single domain)', '+DSTC2_train (domain adapt.)']
By comparing the column “DSTC3_train” with “+DSTC2_train”, we can see performances on the target domain are improved dramatically due to that the parameters are pre-trained on the source domain. By comparing the baselines with our proposed methods, most of our methods can outperform all baselines on both single domain and domain adaptation tasks. Notably, by using label embeddings extracted from slot exemplars with the pre-trained biLMs of ELMo, the slot filling model can achieve the best results (78.58% and 87.55%).