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We present the first corpus of Alsatian Alemannic dialects following Universal Dependencies (UD) guidelines, a project which already covers many of the world's languages. Standard languages are represented to a greater extent than non-standard varieties in UD, and our corpus contributes to closing the gap in the lack of resources for Alsatian dialects by presenting the first UD treebank for these dialects, which are spoken in Northeastern France. Our corpus is annotated both with part-of-speech tags and dependency information, as well as French glosses and German lemmas, containing in total 975 sentences and 19,286 tokens, spanning over various text genres. In this article, we present our data, details of the annotation process, as well as some specific syntactic phenomena which differentiate and situate Alsatian with regards to both Standard German and some other German non-standard varieties. The addition of this corpus to the UD project allows for a higher visibility of the Alemannic Alsatian dialects in linguistic research, and provides a valuable resource for research in many fields, including NLP, syntax and comparative Germanic linguistics.

Aligned corpus of Standard Breton and Standard French, coming from the mailing list of an associative structure, Kontañ-kaoz in Plogonnec in Finistère. Yann-Bêr Rivalin is the creator of the original bilingual material. Mélanie Jouitteau aligned both texts and very lightly edited inconsistencies and typos. The corpus is not anonymized, but the content is that of public announcements.

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.

Align text with Breton speech, create subtitles

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Anaouder mouezh e Brezhoneg gant Vosk

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.

Automatic language identification is frequently framed as a multi-class classification problem. However, when creating digital corpora for less commonly written languages, it may be more appropriate to consider it a data mining problem. For these varieties, one knows ahead of time that the vast majority of documents are of little interest. By minimizing resources spent on classifying such documents, we can create corpora much faster and with better coverage than using established pipelines. To demonstrate the effectiveness of the language mining perspective, we introduce a new pipeline and corpora for several French-based Creoles.

Bien que le dagur et l’alsacien représentent deux familles de langues typologiquement éloignées, ils partagent plusieurs similitudes : les deux langues sont en danger, n’ont pas de système orthographique unifié, et ont peu de corpus numériques disponibles. Compte tenu de ces défis, l’objectif principal de cet article est de comparer le bruit dans les corpus de ces deux langues et son impact sur l’annotation et l’étiquetage des parties du discours (POS). Nous discutons d’abord des stratégies qui peuvent être utilisées pour réduire le bruit dû aux incohérences orthographiques observées lors de la collecte des corpus, en utilisant le dagur comme exemple. Nous observons ensuite que les distributions des trigrammes POS dans les corpus manuellement annotés de dagur et d’alsacien sont similaires à celles des langues typologiquement apparentées dans UD v2.12, ce qui justifie l’expérimentation d’approches de transfert zéro-shot pour l’étiquetage morphosyntaxique. Nous évaluons quelques stratégies simples de réduction du bruit pour l’étiquetage morphosyntaxique en utilisant l’exemple des dialectes alsaciens et en nous basant sur leur proximité avec l’allemand standard. Les résultats obtenus confirment le rôle important de la proximité linguistique dans l’étiquetage morphosyntaxique et l’efficacité de la méthode de transformation des données que nous proposons. Cependant, ils invitent également à une interprétation plus poussée des capacités des modèles multilingues.

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.

Notre objectif est de développer un système de reconnaissance automatique de la parole (ASR) de langues régionales. Pour cela, nous explorons la spécialisation ou l’adaptation de Whisper par affinage (fine-tuning). Dans cet article, nous présentons un retour d’expérience sur des travaux en cours dans deux langues : le basque et l’alsacien.

We present a novel, open-access dataset designed for semantic layout analysis, built to support document recreation workflows through mapping with the Text Encoding Initiative (TEI) standard. This dataset includes 7,254 annotated pages spanning a large temporal range (1600-2024) of digitised and born-digital materials across diverse document types (magazines, papers from sciences and humanities, PhD theses, monographs, plays, administrative reports, etc.) sorted into modular subsets. By incorporating content from different periods and genres, it addresses varying layout complexities and historical changes in document structure. The modular design allows domain-specific configurations. We evaluate object detection models on this dataset, examining the impact of input size and subset-based training. Results show that a 1280-pixel input size for YOLO is optimal and that training on subsets generally benefits from incorporating them into a generic model rather than fine-tuning pre-trained weights.

Ce poster explore les défis de l'annotation syntaxique pour l'alsacien, une langue peu dotée, en comparant deux approches novatrices. D'un côté, nous examinons l'utilisation des grands modèles de langue (LLMs) génératifs, tels que ChatGPT ou Mistral, qui promettent une couverture linguistique large mais potentiellement superficielle. De l'autre, nous étudions des modèles plus légers de type encodeur, entraînés spécifiquement sur des langues proches de l'alsacien. Notre analyse met en lumière les forces et les faiblesses de chaque méthode, en examinant leur efficacité et leur capacité à saisir les subtilités de la syntaxe alsacienne. L'objectif est de déterminer si la "wunderbàr" technologie des LLMs écrase la concurrence, ou si les modèles plus modestes, nourris à la "choucroute neuronale" des langues voisines, peuvent rivaliser pour dompter la grammaire alsacienne. Cette recherche vise ainsi à ouvrir de nouvelles perspectives pour l'annotation syntaxique des langues peu dotées et à contribuer au développement d'outils linguistiques plus performants pour l'alsacien. Préparez-vous à assister à un combat épique entre modèles d'IA pour conquérir la syntaxe alsacienne !

Abstract Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research. While the genealogical ties between Creoles and a number of highly resourced languages imply a significant potential for transfer learning, this potential is hampered due to this lack of annotated data. In this work we present CreoleVal, a collection of benchmark datasets spanning 8 different NLP tasks, covering up to 28 Creole languages; it is an aggregate of novel development datasets for reading comprehension relation classification, and machine translation for Creoles, in addition to a practical gateway to a handful of preexisting benchmarks. For each benchmark, we conduct baseline experiments in a zero-shot setting in order to further ascertain the capabilities and limitations of transfer learning for Creoles. Ultimately, we see CreoleVal as an opportunity to empower research on Creoles in NLP and computational linguistics, and in general, a step towards more equitable language technology around the globe.

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.

Whisper and other large-scale automatic speech recognition models have made significant progress in performance. However, their performance on many low-resource languages, such as Kazakh, is not satisfactory. It is worth researching how to utilize low-cost data to improve the performance of Whisper on under-represented languages. In this study, we utilized easily accessible unpaired speech and text data and combined the language model GPT with Whisper on Kazakh. We implemented end of transcript (EOT) judgment modification and hallucination penalty to improve the performance of speech recognition. Further, we employed the decoding average token log probability as a criterion to select samples from unlabeled speech data and used pseudo-labeled data to fine-tune the model to further improve its performance. Ultimately, we achieved more than 10\% absolute WER reduction in multiple experiments, and the whole process has the potential to be generalized to other under-represented languages.

Whether or not several Creole languages which developed during the early modern period can be considered genetic descendants of European languages has been the subject of intense debate. This is in large part due to the absence of evidence of intermediate forms. This work introduces a new open corpus, the Moly\'e corpus, which combines stereotypical representations of three kinds of language variation in Europe with early attestations of French-based Creole languages across a period of 400 years. It is intended to facilitate future research on the continuity between contact situations in Europe and Creolophone (former) colonies.

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.

Everlasting contact between language communities leads to constant changes in languages over time, and gives rise to language varieties and dialects. However, the communities speaking non-standard language are often overlooked by non-inclusive NLP technologies. Recently, there has been a surge of interest in studying diatopic and diachronic changes in dialect NLP, but there is currently no research exploring the intersection of both. Our work aims to fill this gap by systematically reviewing diachronic and diatopic papers from a unified perspective. In this work, we critically assess nine tasks and datasets across five dialects from three language families (Slavic, Romance, and Germanic) in both spoken and written modalities. The tasks covered are diverse, including corpus construction, dialect distance estimation, and dialect geolocation prediction, among others. Moreover, we outline five open challenges regarding changes in dialect use over time, the reliability of dialect datasets, the importance of speaker characteristics, limited coverage of dialects, and ethical considerations in data collection. We hope that our work sheds light on future research towards inclusive computational methods and datasets for language varieties and dialects.