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This paper presents a fine-tuning approach for automatic speech recognition (ASR) that exploits linguistic similarities between dialects to address data scarcity and imbalance in dialectally rich, low-resource languages. The approach is evaluated on Occitan, a low-resource language composed of six main dialects with unevenly distributed resources. For the better-resourced dialects, Languedocien and Gascon, a two-step fine-tuning is applied to improve recognition performance by exploiting inter-dialectal information. For the less-represented dialects, we assess the effectiveness of knowledge transfer from the better-resourced variants, analyzing the model’s ability to generalize to sparsely represented dialects. The results show that leveraging linguistic proximity between dialects is an effective strategy to enhance ASR performance in low-resource settings with high dialectal variability.

We introduce ForumOccitania, a new Occitan corpus of posts from an online forum, covering a range of topics and dialects. While some existing datasets for this low-resource language include labels of varieties within the dialect continuum, we go one step further by providing metadata pertaining to sociolinguistic factors of language variation (dialect, geographical location, age, proficiency), extracted from self-declared user profiles. We carry out statistical and qualitative analyses, as well as preliminary experiments on unsupervised dialect identification. Our results show that (i) most of the contents is written in Occitan, with the classical spelling conventions, and by young new speakers, (ii) posts display a strong presence of dialectal features from four major Occitan varieties (Lemosin, Lengadocian, Gascon, Provençau), and (iii) a simple topic modelling approach introduced by Kuparinen and Scherrer (2024) effectively detects salient features of these four varieties, but also reveals finer-grained diatopical variation tendencies.

Occitan is a Romance language spoken mostly in the South of France and characterised by rich dialectal variation, which can pose problems for certain NLP tools. This shortfall is largely attributable to the scarcity of dialect-annotated corpora, in a context where linguistic classification within the Occitan dialect continuum is still debated and major nomenclatures, such as ISO 639, fail to provide granular codes for varieties below the generic “Occitan” label. In this paper, we introduce OcWikiDialects, a new dataset comprising articles from the Occitan Wikipedia. The corpus features rich metadata, including dialect labels, and is segmented at both paragraph and sentence levels. Combined with previously released datasets, we explore approaches for Occitan dialect identification by training three types of model on up to 8 labels: linear SVM classifiers based on word and character n-grams, FastText classifiers based on pretrained vectors, and BERT-based neural classifiers adapted through fine-tuning. Evaluations across in- and out-of-domain test sets demonstrate the substantial impact of our new dataset for the task. However, a peak macro-averaged F1 score of 58.15 underscores persistent challenges for underrepresented Occitan varieties, supported by our per-dialect analysis. Code, dataset and models are available: https://github.com/DEFI-COLaF/OcWikiDialects.

Description du processus de développement et d’anonymisation d’un corpus en occitan issu d’un forum en ligne et riche en métadonnées liées à la variation dialectale.

Automatic Speech Recognition (ASR) has advanced considerably in recent years, largely fueled by breakthroughs in self-supervised learning and the increasing availability of large-scale multilingual speech corpora. Models such as Whisper have demonstrated remarkable performance across numerous languages. However, despite its strong multilingual coverage, performance remains highly variable across languages. In this study, we fine-tune Whisper in four regional languages spoken in France, each selected to represent distinct linguistic and resource conditions: Basque, Occitan, Alsatian, and Shimaore. Performance is increased by fine-tuning for each language, but to significantly varying degrees. This is due to a combination of varying data quantity and quality, language similarity to an already well-covered language, and transcription standardisation: all things that low-resource languages may lack.

Cet article dresse un état de l'art de la traduction automatique et de son évaluation pour les langues à variation dialectale, et en particulier pour les continuums dialectaux. Pour illustrer cet état de l'art, nous proposons une série d'expériences préliminaires sur le continuum occitan, afin de dresser un état des performances des systèmes existants pour la traduction depuis et vers plusieurs variétés d'occitan. Nos résultats indiquent d'une part des performances globalement satisfaisantes pour la traduction vers le français et l'anglais. D'autre part, des analyses mélangées à des outils d'identification de langues sur les prédictions vers l'occitan mettent en lumière la capacité de la plupart des systèmes évalués à générer des textes dans cette langue (y compris en zero-shot ), mais révèlent aussi des limitations en termes d'évaluation de la diversité dialectale dans les traductions proposées.

Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing, yet their effectiveness in handling historical languages remains largely unexplored. This study examines the performance of open-source LLMs in part-of-speech (POS) tagging for Old Occitan, a historical language characterized by non-standardized orthography and significant diachronic variation. Through comparative analysis of two distinct corpora-hagiographical and medical texts-we evaluate how current models handle the inherent challenges of processing a low-resource historical language. Our findings demonstrate critical limitations in LLM performance when confronted with extreme orthographic and syntactic variability. We provide detailed error analysis and specific recommendations for improving model performance in historical language processing. This research advances our understanding of LLM capabilities in challenging linguistic contexts while offering practical insights for both computational linguistics and historical language studies.

Dieser Artikel stellt Überlegungen zu den Herausforderungen der Dokumentation von Minderheitensprachen im digitalen Raum an, ausgehend von den Arbeiten, die im Rahmen des DIVITAL-Projekts durchgeführt wurden. Die ersten Arbeiten des Projekts betrafen die Sammlung von Korpora und ihre Dokumentation durch feinkörnige Metadaten. Diese Arbeiten haben zwei große Herausforderungen aufgezeigt: (i) die Identifizierung der Sprachen und ihrer Varianten im Rahmen der Normen für die Kodierung von Sprachnamen und (ii) die Schaffung neuer Ressourcen in Verbindung mit der aktuellen Praxis dieser Sprachen.

Word classes are linguistic categories serving as basis in the description of the vocabulary and grammar of natural languages. While important publications are regularly devoted to their definition, identification, and classification, in the field of Romance linguistics we lack a comprehensive, state-of-the-art overview of the current research. This Manual offers an updated and detailed discussion of all relevant aspects related to word classes in the Romance languages. In the first part, word classes are discussed from both a theoretical and historical point of view. The second part of the volume takes as its point of departure single word classes, described transversally in all the main Romance languages, while the third observes the relevant word classes from the point of view of specific Romance(-based) varieties. The fourth part explores Romance word classes at the interface of grammar and other fields of research. The Manual is intended as a reference work for all scholars and students interested in the description of both the standard, major Romance languages and the smaller, lesser described Romance(-based) varieties.

The Manual offers the first comprehensive overview on Occitan and Occitan linguistics, including the latest research. With 26 contributions organized in seven parts, it covers diachronic and synchronic language description, diatopic variation and sociolinguistics, language planning and equipment, as well as teaching and language pedagogy.

A python programme to tokenise texts in Occitan based on rules. To launch the programme, execute the following instruction: python3 tokenizer_occitan.py < input.txt > output.conllu The script takes as input a text file with a single sentence per line, starting by a sentence ID, followed by a tab character, followed by the sentence itself. The current version of the tool was developped during the projects DIVITAL (funded by the ANR) and CorCoDial (funded by the Academy of Finland).

Effectively normalizing spellings in textual data poses a considerable challenge, especially for low-resource languages lacking standardized writing systems. In this study, we fine-tuned a multilingual model with data from several Occitan dialects and conducted a series of experiments to assess the model's representations of these dialects. For evaluation purposes, we compiled a parallel lexicon encompassing four Occitan dialects.Intrinsic evaluations of the model's embeddings revealed that surface similarity between the dialects strengthened representations. When the model was further fine-tuned for part-of-speech tagging, its performance was robust to dialectical variation, even when trained solely on part-of-speech data from a single dialect. Our findings suggest that large multilingual models minimize the need for spelling normalization during pre-processing.

This paper describes different approaches for developing, for the first time, an automatic speech recognition system for two of the main dialects of Occitan, namely Gascon and Languedocian, and the results obtained in them. The difficulty of the task lies in the fact that Occitan is a less-resourced language. Although a great effort has been made to collect or create corpora of each variant (transcribed speech recordings for the acoustic models and two text corpora for the language models), the sizes of the corpora obtained are far from those of successful systems reported in the literature, and thus we have tested different techniques to compensate for the lack of resources. We have developed classical systems using Kaldi, creating an acoustic model for each variant and also creating language models from the collected corpora and from machine translated texts. We have also tried fine-tuning a Whisper model with our speech corpora. We report word error rates of 20.86 for Gascon and 13.52 for Languedocian with the Kaldi systems and 16.37 for Gascon and 11.74 for Languedocian with Whisper.

The Occitan language is a less resourced language and is classified as `in danger' by the UNESCO. Thereby, it is important to build resources and tools that can help to safeguard and develop the digitisation of the language. CorpusArièja is a collection of 72 texts (just over 41,000 tokens) in the Occitan language of the French department of Ariège. The majority of the texts needed to be digitised and pass within an Optical Character Recognition. This corpus contains dialectal and spelling variation, but is limited to prose, without diachronic variation or genre variation. It is an annotated corpus with two levels of lemmatisation, POS tags and verbal inflection. One of the main aims of the corpus is to enable the conception of tools that can automatically annotate all Occitan texts, regardless of the dialect or spelling used. The Ariège territory is interesting because it includes the two variations that we focus on, dialectal and spelling. It has plenty of authors that write in their native language, their variety of Occitan.

Parallel corpora are still scarce for most of the world's language pairs. The situation is by no means different for regional languages of France. In addition, adequate web interfaces facilitate and encourage the use of parallel corpora by target users, such as language learners and teachers, as well as linguists. In this paper, we describe ParCoLab, a parallel corpus and a web platform for querying the corpus. From its onset, ParCoLab has been geared towards lower-resource languages, with an initial corpus in Serbian, along with French and English (later Spanish). We focus here on the extension of ParCoLab with a parallel corpus for four regional languages of France: Alsatian, Corsican, Occitan and Poitevin-Saintongeais. In particular, we detail criteria for choosing texts and issues related to their collection. The new parallel corpus contains more than 20k tokens per regional language.

This paper presents Loflòc (Lexic obèrt flechit Occitan – Open Inflected Lexicon of Occitan), a morphological lexicon for Occitan. Even though the lexicon no longer occupies the same place in the NLP pipeline since the advent of large language models, it remains a crucial resource for low-resourced languages. Occitan is a Romance language spoken in the south of France and in parts of Italy and Spain. It is not recognized as an official language in France and no standard variety is shared across the area. To the best of our knowledge, Loflòc is the first publicly available lexicon for Occitan. It contains 650 thousand entries for 57 thousand lemmas. Each entry is accompanied by the corresponding Universal Dependencies Part-of-Speech tag. We show that the lexicon has solid coverage on the existing freely available corpora of Occitan in four major dialects. Coverage gaps on multi-dialect corpora are overwhelmingly driven by dialectal variation, which affects both open and closed classes. Based on this analysis we propose directions for future improvements.

Cet ouvrage rassemble des travaux menés de 2019 à 2020 en lien avec un projet de recherche du Centre d'études franco-russes du CNRS sur les noms des variantes de langue minoritaire.

While existing neural network-based approaches have shown promising results in Handwritten Text Recognition (HTR) for high-resource languages and standardized/machine-written text, their application to low-resource languages often presents challenges, resulting in reduced effectiveness. In this paper, we propose an innovative HTR approach that leverages the Transformer architecture for recognizing handwritten Old Occitan language. Given the limited availability of data, which comprises only word pairs of graphical variants and lemmas, we develop and rely on elaborate data augmentation techniques for both text and image data. Our model combines a custom-trained Swin image encoder with a BERT text decoder, which we pre-train using a large-scale augmented synthetic data set and fine-tune on the small human-labeled data set. Experimental results reveal that our approach surpasses the performance of current state-of-the-art models for Old Occitan HTR, including open-source Transformer-based models such as a fine-tuned TrOCR and commercial applications like Google Cloud Vision. To nurture further research and development, we make our models, data sets, and code publicly available.