Few-Shot and Zero-Shot Learning for Historical Text Normalization
Table 3: Normalization accuracy on dev sets for zero-shot experiments. Best results highlighted in bold.
['[BOLD] Dataset', '[BOLD] Identity', '[BOLD] Zero-shot']
The zero-shot approach improves over this baseline for half of the datasets, sometimes by up to 12 percentage points (DER). Micro-averaging the results shows an overall advantage for zero-shot learning. We can also compare the error reduction from MTL to the identity baseline (cf. Tab. The strong correlation suggests that the regularization effect introduced by MTL is particularly helpful with tasks where there is a strong similarity between input and output; or, in other words, that multi-task learning prevents the model from over-generalizing based on the training data.