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We highlight several issues in the evaluation of historical textnormalization systems that make it hard to tell how well these systems wouldactually work in practice---i.e., for new datasets or languages; in comparisonto more na\"ive systems; or as a preprocessing step for downstream NLP tools.We illustrate these issues and exemplify our proposed evaluation practices bycomparing two neural models against a na\"ive baseline system. We show that theneural models generalize well to unseen words in tests on five languages;nevertheless, they provide no clear benefit over the na\"ive baseline fordownstream POS tagging of an English historical collection. We conclude thatfuture work should include more rigorous evaluation, including both intrinsicand extrinsic measures where possible.

Evaluating historical text normalization systems: How well do they generalize?