Can the Language of the Collation be Translated into the Language of the Stemma? Using Machine Translation for Witness Localization

TL;DR

This paper introduces a novel deep learning method for placing manuscripts on stemmas in stemmatology, aiming to improve understanding of textual transmission and potentially extend to phylogenetics.

Contribution

It presents the first known deep learning approach for witness placement in stemmatology, bridging computational methods between stemmatology and phylogenetics.

Findings

Demonstrates potential of DL in stemmatology

Suggests extension to DNA sequence analysis in phylogenetics

Provides a new computational tool for manuscript analysis

Abstract

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.