  Stemmatology is a subfield of philology where one approach to understand the
copy-history of textual variants of a text (witnesses of a tradition) is to
generate an evolutionary tree. Computational methods are partly shared between
the sister discipline of phylogenetics and stemmatology. In 2022, a surveypaper
in nature communications found that Deep Learning (DL), which otherwise has
brought about major improvements in many fields (Krohn et al 2020) has had only
minor successes in phylogenetics and that "it is difficult to conceive of an
end-to-end DL model to directly estimate phylogenetic trees from raw data in
the near future"(Sapoval et al. 2022, p.8). In stemmatology, there is to date
no known DL approach at all. In this paper, we present a new DL approach to
placement of manuscripts on a stemma and demonstrate its potential. This could
be extended to phylogenetics where the universal code of DNA might be an even
better prerequisite for the method using sequence to sequence based neural
networks in order to retrieve tree distances.
