Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking
Table 7: Evaluation results under the few-shot settings. We include 0%, 50%, and 90% dialogues in dev set for training respectively and conduct the evaluation on the remaining dialogues in dev set.
['[EMPTY]', '[BOLD] Active Intent Acc.', '[BOLD] Requested Slot F1', '[BOLD] Average Goal Acc.', '[BOLD] Joint Goal Acc.']
We can find that including samples from dev set for training can have a significant performance improvement on the joint goal prediction, especially for the “unseen APIs” subset, which demonstrates the effects of few-shot learning.