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

arXiv: https://arxiv.org/abs/2206.05603 · HTML: https://mlsanity.com/paper/2206.05603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 11 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [OpenNMT: Neural Machine Translation Toolkit](https://mlsanity.com/paper/1805.11462/) (2018)
- [Probing Multilingual BERT for Genetic and Typological Signals](https://mlsanity.com/paper/2011.02070/) (2020)
- [Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios](https://mlsanity.com/paper/2012.00876/) (2020)
- [Language classification from bilingual word embedding graphs](https://mlsanity.com/paper/1607.05014/) (2016)

## Abstract (arXiv)

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.