# Lost in Translation? Exploring the Shift in Grammatical Gender from Latin to Occitan

arXiv: https://arxiv.org/abs/2605.09156 · HTML: https://mlsanity.com/paper/2605.09156/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 9 May 2026.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [Improving Lemmatization of Non-Standard Languages with Joint Learning](https://mlsanity.com/paper/1903.06939/) (2019)
- [Quantifying the Semantic Core of Gender Systems](https://mlsanity.com/paper/1910.13497/) (2019)
- [Stability of meanings versus rate of replacement of words: an experimental test](https://mlsanity.com/paper/1802.06764/) (2018)
- [Modern Models, Medieval Texts: A POS Tagging Study of Old Occitan](https://mlsanity.com/paper/2503.07827/) (2025)
- [Shaping the Future of Endangered and Low-Resource Languages -- Our Role in the Age of LLMs: A Keynote at ECIR 2024](https://mlsanity.com/paper/2409.13702/) (2024)
- [Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages](https://mlsanity.com/paper/2506.17715/) (2025)

## Abstract (arXiv)

The diachronic evolution from Latin to the Romance languages involved a restructuring of the grammatical gender system from a tripartite configuration (masculine, feminine, neuter) to a bipartite one (masculine, feminine) in most Romance languages. In this work, we introduce an interpretable deep learning framework to investigate this phenomenon at both lexical and contextual levels. First, we show that conventional tokenization strategies are insufficiently robust for this low-resource historical setting, and that our proposed tokenizer improves performance over these baselines. At the lexical level, we evaluate the contribution of morphological features to gender prediction. At the contextual level, we quantify the contributions of different part-of-speech categories to grammatical gender prediction. Together, these analyses characterize the distribution of gender information between the lemma and its sentential context. We make our codebase, datasets, and results publicly available at hrefhttps://github.com/ahan-2000/Lost-in-Translation-https://github.com/ahan-2000/Lost-in-Translation-.