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This paper investigates the curse of multilinguality in lexical normalization, finding that training a single model on multiple languages leads to decreased per-language accuracy, with optimal performance when languages are trained in small groups.
This paper introduces neural approaches for reversing character-set simplification and abbreviation in medieval text, using one-to-one and banded RNNs trained with self-supervision or parallel corpora, and presents a Python library for letter lemmatization.