# Machine Translation for Low-Resource Dialectal Variants

Oriane Nédey, Thibault Clérice, Rachel Bawden, Benoît Sagot

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## To cite this version:

Oriane Nédey, Thibault Clérice, Rachel Bawden, Benoît Sagot. Machine Translation for Low-Resource Dialectal Variants. Journée de l'école doctorale EDITE, Mar 2025, Paris, France. hal-05366306

HAL Id: hal-05366306

https://inria.hal.science/hal-05366306v1

Submitted on 14 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Machine Translation for Low-Resource Dialectal Variants

Oriane Nédey

Supervisors: Benoît Sagot, Rachel Bawden, Thibault Clérice

## Occitan, a dialect continuum

* a language with a long history, no official status in France

* no official standard, but

* Occitan Wikipedia is in Languedocien by default

* most written texts use the “classical” spelling system

* there are Occitan language courses

* high internal **variation**: phonetics/phonology, morphology, syntax, lexicon

* attempts at **classifying into dialects/subdialects**, yet no consensus and no clear boundaries

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Dialectes et sous-dialectes de l'occitan (© Jean Sibilie)

<table><caption>■ nord-occitan, ■ occitan moyen, ■ gascon / 1 gévaudanais, 2 sud-vivarois.</caption><thead><tr><th>Variation type</th><th>Dialect 1</th><th>Dialect 2</th><th>French</th><th>English</th></tr></thead><tbody><tr><td>phonetics/phonology</td><td>Languedocien: Vos <b>cal</b> crear <b>almens</b> una rubrica.</td><td>Niçard: Vos <b>cau</b> crear <b>aumens</b> una rubrica.</td><td>Vous devez créer au moins une rubrique.</td><td>You must create at least one section.</td></tr><tr><td>morphology</td><td>Languedocien: Quora <b>auretz</b> acabat vòstre article, <b>poiretz</b> prepausar que siá publicat.</td><td>Gascon: Quora <b>auratz</b> acabat lo vòste article, que <b>poiratz</b> perpausar que sia publicat.</td><td>Lorsque votre article est terminé, vous pouvez proposer sa publication.</td><td>When your article is finished, you can submit it for publication.</td></tr><tr><td>lexicon</td><td>Languedocien: Lo concors de <b>pocion</b></td><td>Gascon: Lo concors de <b>potinga</b></td><td>Le concours de potion</td><td>The Potion Contest</td></tr><tr><td>syntax</td><td>Languedocien: Podètz administrar aquela rubrica</td><td>Gascon: <b>Que</b> podetz administrar aquera rubrica</td><td>Vous pouvez administrer cette rubrique</td><td>You can manage this section</td></tr><tr><td>spelling convention</td><td>Provençal, graphie classique: Un boquet de <b>floretas</b></td><td>Provençal, graphie mistralienne: Un <b>bouquet de floureto</b></td><td>Un bouquet de fleurettes</td><td>A bouquet of small flowers</td></tr></tbody></table>

Sources

* Aure Séguier, Lo Congrès: "SoftwaresOccitanTranslations corpus". https://zenodo.org/records/8411351 • David Revoy, Aure Séguier: "Pepper&Carrot". https://www.peppercarrot.com/ • Cantar lou país, 100 chansons de Provence: https://www.100chansonsdeprovence.com/

## Limitations with current datasets

* **limited amount** of textual data

* mostly from unrepresentative domains (Wikipedia, software translations)

* very few datasets with dialect labels

* no dataset of contrastive segments (i.e. with equivalents between dialects)

* **frequent alignment issues**: when a reference translation corresponds only partially (or not at all) to the source text

* **code-switching**: multiple languages in the same text (La ligne Imaginot passe aussi par la fête de l'Huma ! dins L'Humanité del 3 de setembre de 1994)

* **unexpected languages**, esp. when data mining was used (e.g. text in Spanish/French instead of Occitan)

## Project: Dataset creation

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## Project: Dialectal MT

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* **Data-centric** approaches:

* normalisation, noise injection, dialect tag, prompt engineering, external resources (lexicons, linguistic taggers)

* **Model-centric** approaches:

* Tokenisation: subwords / characters

* Model type: encoder-decoder / decoder-only

* Training phases: zero-shot, In-Context Learning, Continuous Pretraining, Fine-tuning

* Controlled generation techniques: adapters, reinforcement learning, prompt embedding, latent space manipulation, etc.

## Project: Dialect identification

* granularity: language / spelling / variety

* single label / multi-label / label “other”

* method: list of words, linear classification, embeddings

## MT Evaluation challenges

* Human evaluation: limited availability of experts

* Existing metrics are **not reliable** in low-resource and dialectal contexts (BLEU, chrF, BERTscore, COMET)

* Lack of **gold references**

* SOTA propositions:

* **dialectal gap** metrics: compare scores of predictions from standard/non-standard languages/varieties

* **pseudo-references**: use MT translation from a standard language as reference

* COMET with **additional pretraining** + **noise injection** during fine-tuning (Aepli et al, 2024)

### References

* Esher, Louise, et Jean Sibilie, éd. Manuel de linguistique occitan. De Gruyter, 2024. https://doi.org/10.1515/9783110733433
* Costa-Jussà, Marta R., James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elae Kalbassi, et al. « Scaling Neural Machine Translation to 200 Languages ». Nature 630, no 8018 (juin 2024): 841-46. https://doi.org/10.1038/s41586-024-0735-x
* Mager, Manuel, Elisabeth Mager, Katharina Kann, et Ngoc Thang Vu. « Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers ». In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), édité par Anna Rogers, Jordan Boyd-Graber, et Naoki Okazaki, 4871-97. Toronto, Canada: Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2023.acl-long.268
* Chifu, Adrian-Gabriel, Goran Glavaš, Radu Tudor Ionescu, Nikola Ljubešić, Aleksandra Miletic, Filip Miletic, Yves Scherrer, et Ivan Vulić. « VarDial Evaluation Campaign 2024: Commonsense Reasoning in Dialects and Multi-Label Similar Language Identification ». In Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024), édité par Yves Scherrer, Tommi Jauhiainen, Nikola Ljubešić, Marcos Zampieri, Preslav Nakov, et Jörg Tiedemann, 1-15. Mexico City, Mexico: Association for Computational Linguistics, 2024. https://doi.org/10.18653/v1/2024.vardial-11
* Kuparinen, Olli, Aleksandra Miletic, et Yves Scherrer. « Dialect-to-Standard Normalization: A Large-Scale Multilingual Evaluation ». In Findings of the Association for Computational Linguistics: EMNLP 2023, édité par Houda Bouamor, Juan Pino, et Kalika Ball, 13814-28. Singapore: Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.923
* Carme Armentano i Oller, et Mikel L. Forcada. « Open Source Machine Translation between Small Languages: Catalan and Aranese Occitan ». In Proceedings of the 5th Workshop on Strategies for Developing Machine Translation for Minority Languages, 51-54. Genoa, Italy, 2006. https://aclanthology.org/2006-arl-117
* Lambrecht, Louisa, Felix Schneider, et Alexander Waibel. « Machine Translation from Standard German to Alemannic Dialects ». In Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages, édité par Maite Melero, Sakriani Sakti, et Claudia Soria, 129-36. Marseille, France: European Language Resources Association, 2022. https://aclanthology.org/2022.sigu-117
* Bafna, Niyati, Emily Chang, Nathaniel R. Robinson, David R. Mortensen, Kenton Murray, David Yarowsky, et Hole Sirin. « DialUp: Modeling the Language Continuum by Adapting Models to Dialects and Dialects to Models ». arXiv, 27 janvier 2025. https://doi.org/10.48550/arXiv.2501.16581
* Liang, Xun, Hanju Wang, Yehzaohui Wang, Shichao Song, Jiawei Yang, Simin Niu, Jie Hu, et al. « Controllable Text Generation for Large Language Models: A Survey ». arXiv, 22 août 2024. https://doi.org/10.48550/arXiv.2408.12599
* Alam, Md Mahfuz Ibn, Sina Ahmadi, et Antonios Anastasopoulos. « CODET: A Benchmark for Contrastive Dialect Evaluation of Machine Translation ». arXiv, 2 février 2024. https://doi.org/10.48550/arXiv.2305.17267
* Faisal, Fahim, Orevaoghene Ahia, Aarohi Srivastava, Kabir Ahuja, David Chiang, Yulia Tsvetkov, et Antonios Anastasopoulos. « DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages ». arXiv, 16 mars 2024. https://doi.org/10.48550/arXiv.2305.11009
* Aepli, Noëmi, Chantal Amrhein, Florian Schottmann, et Rico Sennrich. « A Benchmark for Evaluating Machine Translation Metrics on Dialects without Standard Orthography ». In Proceedings of the Eighth Conference on Machine Translation, édité par Philipp Koehn, Barry Haddow, Tom Kocmi, et Christof Monz, 1045-65. Singapore: Association for Computational Linguistics, 2023. https://doi.org/10.18653/v1/2023.wmt-1.99

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PaR[AI]RIE

Inria, ALMANACH, Défi-COLAF, PRAIRIE Institute

Contact:
<oriane.nedey@inria.fr>