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

MCML Authors

Matthias Aßenmacher

Dr.

→ Group Bernd Bischl
 Statistical Learning and Data Science

Esteban Garces Arias

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

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 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.

misc CSA+26

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Preprint

May. 2026

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Authors

A. Chatterjee • M. Schöffel • M. Aßenmacher • E. Garces Arias

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Links

arXiv GitHub

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Research Area

 A1 | Statistical Foundations & Explainability