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An interpretable deep learning framework quantifies how grammatical gender information splits between word lemmas and sentence context during the Latin-to-Occitan shift.

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2026-06-30 22:50 UTC pith:PN6SNM2B

load-bearing objection Custom tokenizer plus lexical/contextual gender attribution for Latin-Occitan, but the abstract supplies no numbers so the actual gains and reliability stay unclear. the 1 major comments →

arxiv 2605.09156 v2 pith:PN6SNM2B submitted 2026-05-09 cs.CL cs.AI

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

Ahan Chatterjee , Matthias Sch\"offel , Matthias A{\ss}enmacher , Marinus Wiedner , Esteban Garces Arias This is my paper
classification cs.CL cs.AI
keywords grammatical genderLatinOccitandeep learningtokenizationhistorical linguisticsRomance languagesinterpretable models
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to measure the move from Latin's three-gender system to Occitan's two-gender system by tracking where gender cues reside in the data. It first tests tokenization methods on these low-resource historical texts and finds that a custom approach outperforms standard ones. The model then checks how much word shape alone predicts gender and how much different parts of speech in the sentence add. This produces a split accounting of lexical versus contextual sources of gender. A reader would care because the method turns an abstract historical change into measurable contributions that can be inspected and compared.

Core claim

The authors introduce an interpretable deep learning framework to investigate the restructuring of grammatical gender from a tripartite system in Latin to a bipartite system in Occitan at both lexical and contextual levels. They show that conventional tokenization strategies are insufficiently robust for this low-resource historical setting and that their proposed tokenizer improves performance over these baselines. At the lexical level the framework evaluates the contribution of morphological features to gender prediction; at the contextual level it quantifies the contributions of different part-of-speech categories, thereby characterizing the distribution of gender information between the

What carries the argument

An interpretable deep learning framework that measures morphological features for lexical gender prediction and part-of-speech contributions for contextual gender prediction.

Load-bearing premise

The available Latin and Occitan datasets are representative enough for the model to measure the split of gender information between lemmas and context without large domain shift or annotation artifacts.

What would settle it

Retraining the model on an independently collected and annotated collection of Latin and Occitan sentences and obtaining substantially different morphological or part-of-speech contribution patterns would falsify the reported distribution of gender information.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Conventional tokenization strategies are insufficiently robust for low-resource historical languages such as Latin and Occitan.
  • The proposed tokenizer improves performance over standard baselines on these texts.
  • Morphological features contribute measurably to gender prediction at the lexical level.
  • Different part-of-speech categories contribute differently to gender prediction at the contextual level.
  • Gender information is distributed between the lemma and its sentential context in a way that the framework can quantify.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same separation of lexical and contextual contributions could be applied to other pairs of historical languages to trace parallel grammatical changes.
  • The tokenizer improvement suggests that domain-specific preprocessing may be necessary before applying modern NLP tools to pre-modern texts.
  • Quantifying how much gender lives in context versus form might help design better models for translating or analyzing older stages of languages.
  • Extending the part-of-speech analysis to additional grammatical categories such as case or number could reveal broader patterns of feature loss.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 0 minor

Summary. The paper introduces an interpretable deep learning framework to investigate the diachronic shift in grammatical gender from Latin (tripartite) to Occitan (bipartite) at both lexical and contextual levels. It claims that conventional tokenization strategies are insufficiently robust for this low-resource historical setting and that a proposed tokenizer improves performance over baselines. At the lexical level, morphological features' contributions to gender prediction are evaluated; at the contextual level, contributions of different POS categories are quantified to characterize the distribution of gender information between the lemma and sentential context. The codebase, datasets, and results are released publicly.

Significance. If the empirical results and analyses hold, the work could provide a reproducible method for attributing grammatical gender information in historical low-resource languages using interpretable models, potentially aiding digital humanities research on language change. The public release of code, data, and results is a clear strength that supports verification and extension. However, the absence of any quantitative results, baselines, error bars, dataset sizes, or performance metrics in the provided text prevents assessment of whether the tokenizer improvement or attribution analyses are meaningful or robust.

major comments (1)
  1. [Abstract] Abstract: the central claims that the proposed tokenizer 'improves performance over these baselines' and that the framework 'quantif[ies] the contributions' of features and POS categories are asserted without any numbers, tables, baselines, statistical tests, or dataset details. This prevents evaluation of whether the claims are supported and makes the soundness of the empirical study impossible to assess from the manuscript as presented.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. The single major comment concerns the abstract's lack of quantitative support for its claims. We address this directly below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claims that the proposed tokenizer 'improves performance over these baselines' and that the framework 'quantif[ies] the contributions' of features and POS categories are asserted without any numbers, tables, baselines, statistical tests, or dataset details. This prevents evaluation of whether the claims are supported and makes the soundness of the empirical study impossible to assess from the manuscript as presented.

    Authors: We agree that the abstract asserts performance improvements and quantification results without supporting numbers or dataset details, which limits immediate evaluability. The full manuscript includes dedicated results sections with tables reporting tokenizer performance against baselines (including accuracy/F1 deltas), dataset sizes for Latin and Occitan corpora, morphological feature attributions, and POS contribution quantifications with associated statistical details. To strengthen the abstract, we will revise it to include concise quantitative highlights (e.g., key performance gains and corpus sizes) while preserving its length constraints. This revision will be made in the next version. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper is an empirical study introducing a tokenizer and analyzing lexical vs. contextual contributions to gender prediction in Latin/Occitan texts via deep learning. No equations, fitted parameters renamed as predictions, self-definitional steps, or load-bearing self-citation chains appear in the abstract or described framework. The work relies on public datasets/code and reports performance improvements over baselines, making the central claims externally falsifiable rather than reducing to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only abstract available; no mathematical derivations, free parameters, or invented entities identifiable. Empirical DL study with no explicit axioms stated.

pith-pipeline@v0.9.1-grok · 5729 in / 1054 out tokens · 33314 ms · 2026-06-30T22:50:25.416582+00:00 · methodology

0 comments
read the original 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 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 \href{https://github.com/ahan-2000/Lost-in-Translation-}{https://github.com/ahan-2000/Lost-in-Translation-}.

Figures

Figures reproduced from arXiv: 2605.09156 by Ahan Chatterjee, Esteban Garces Arias, Marinus Wiedner, Matthias A{\ss}enmacher, Matthias Sch\"offel.

Figure 1
Figure 1. Figure 1: Map representing the spread of the Occitan [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Gender Shift Frequencies across all three [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Examples of hybrid tokenization capturing or [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Proposed Architecture to assess the impact [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: SHAP summary plot for the best-performing [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example in which the lemma-only model mis [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Gender shift frequencies for different Lemma endings. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Attention-based contextual evidence for grammatical gender prediction shown for two representative [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: SHAP beeswarm plot showing feature contributions to model error prediction. Each dot represents a [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗

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Reference graph

Works this paper leans on

3 extracted references · 1 canonical work pages

  1. [1]

    Improving lemmatization of non-standard lan- guages with joint learning. InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, Volume 1 (Long and Short Papers), pages 1493–1503, Minneapolis, Min- nesota. Association for Computational Linguistics. Daniela Marzo an...

    2019

  2. [2]

    InProceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024, pages 66–71, Torino, Italia

    CorpusArièja: Building an annotated cor- pus with variation in Occitan. InProceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024, pages 66–71, Torino, Italia. ELRA and ICCL. Elton Prifti, Wolfgang Schweickard, Maria Selig, and Sabine Tittel. 2023. Sprachdatenbasierte model- lierung von wissensne...

    work page arXiv 2024

  3. [3]

    A Data Description A.1 Gender Shift by Lemma Ending Figure 7: Gender shift frequencies for different lemma endings

    Natural language processing for similar lan- guages, varieties, and dialects: A survey.Natural Language Engineering, 26(6):595–612. A Data Description A.1 Gender Shift by Lemma Ending Figure 7: Gender shift frequencies for different lemma endings. A.2 Lexical Diversity in Raw Occitan Texts File Tokens Types TTR MATTR@50 MATTR@100 MATTR@500 Harley_3041.txt...

    2025