Few-Shot Natural Language Generation by Rewriting Templates
Table 6: Performance in few-shot settings. K-shot denotes K dialogues for an API in the training set.
['[BOLD] Model', '[BOLD] Metric', '[BOLD] 5-shot', '[BOLD] 10-shot', '[BOLD] 20-shot', '[BOLD] 40-shot', '[BOLD] 80-shot', '[BOLD] All Data']
In all k-shot settings, T2G2 gives consistent improvements of 3-4 BLEU while reducing the SER by 50%. Even in the extreme 5-shot setting, the SER is just 2.66%. Remarkably, T2G2 in the 20-shot setting (280 dialogs total) performs on par with the Naive model trained on the entire dataset which is 20X larger (5,403 dialogs). We take this as evidence that our templatized input representation can lead to significant reduction in labelled data requirements.