  Historical text normalization often relies on small training datasets. Recent
work has shown that multi-task learning can lead to significant improvements by
exploiting synergies with related datasets, but there has been no systematic
study of different multi-task learning architectures. This paper evaluates
63~multi-task learning configurations for sequence-to-sequence-based historical
text normalization across ten datasets from eight languages, using
autoencoding, grapheme-to-phoneme mapping, and lemmatization as auxiliary
tasks. We observe consistent, significant improvements across languages when
training data for the target task is limited, but minimal or no improvements
when training data is abundant. We also show that zero-shot learning
outperforms the simple, but relatively strong, identity baseline.
