  Text normalization is a crucial technology for low-resource languages which
lack rigid spelling conventions or that have undergone multiple spelling
reforms. Low-resource text normalization has so far relied upon hand-crafted
rules, which are perceived to be more data efficient than neural methods. In
this paper we examine the case of text normalization for Ligurian, an
endangered Romance language. We collect 4,394 Ligurian sentences paired with
their normalized versions, as well as the first open source monolingual corpus
for Ligurian. We show that, in spite of the small amounts of data available, a
compact transformer-based model can be trained to achieve very low error rates
by the use of backtranslation and appropriate tokenization.
