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Metadata are key components of language resources and facilitate their exploitation and re-use. Their creation is a labour intensive process and requires a modeling step, which identifies resource-specific information as well as standards and controlled vocabularies that can be reused. In this article, we focus on metadata for documenting text bases for regional languages of France characterised by several levels of variation (space, time, usage, social status), based on a survey of existing metadata schema. Moreover, we implement our metadata model as a database structure for the Heurist data management system, which combines both the ease of use of spreadsheets and the ability to model complex relationships between entities of relational databases. The Heurist template is made freely available and was used to describe metadata for text bases in Alsatian and Poitevin-Santongeais. We also propose tools to automatically generate XML metadata headers files from the database.

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.

Language identification is an important first step in many NLP applications. Most publicly available language identification datasets, however, are compiled under the assumption that the gold label of each instance is determined by where texts are retrieved from. Research has shown that this is a problematic assumption, particularly in the case of very similar languages (e.g., Croatian and Serbian) and national language varieties (e.g., Brazilian and European Portuguese), where texts may contain no distinctive marker of the particular language or variety. To overcome this important limitation, this paper presents DSL True Labels (DSL-TL), the first human-annotated multilingual dataset for language variety identification. DSL-TL contains a total of 12,900 instances in Portuguese, split between European Portuguese and Brazilian Portuguese; Spanish, split between Argentine Spanish and Castilian Spanish; and English, split between American English and British English. We trained multiple models to discriminate between these language varieties, and we present the results in detail. The data and models presented in this paper provide a reliable benchmark toward the development of robust and fairer language variety identification systems. We make DSL-TL freely available to the research community.

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.

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.

We introduce Mediapi-RGB, a new dataset of French Sign Language (LSF) along with the first LSF-to-French machine translation model. With 86 hours of video, it the largest LSF corpora with translation. The corpus consists of original content in French Sign Language produced by deaf journalists, and has subtitles in written French aligned to the signing. The current release of Mediapi-RGB is available at the Ortolang corpus repository, and can be used for academic research purposes. The test and validation sets contain 13 and 7 hours of video respectively. The training set contains 66 hours of video that will be released progressively until December 2024. Additionally, the current release contains skeleton keypoints, sign temporal segmentation, spatio-temporal features and subtitles for all the videos in the train, validation and test sets, as well as a suggested vocabulary of nouns for evaluation purposes. In addition, we present the results obtained on this corpus with the first LSF-to-French translation baseline to give an overview of the possibilities offered by this corpus of unprecedented caliber for LSF. Finally, we suggest potential technological and linguistic applications for this new video-text dataset.

State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets. This survey delves into an important attribute of these datasets: the dialect of a language. Motivated by the performance degradation of NLP models for dialectic datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches. We describe a wide range of NLP tasks in terms of two categories: natural language understanding (NLU) (for tasks such as dialect classification, sentiment analysis, parsing, and NLU benchmarks) and natural language generation (NLG) (for summarisation, machine translation, and dialogue systems). The survey is also broad in its coverage of languages which include English, Arabic, German among others. We observe that past work in NLP concerning dialects goes deeper than mere dialect classification, and . This includes early approaches that used sentence transduction that lead to the recent approaches that integrate hypernetworks into LoRA. We expect that this survey will be useful to NLP researchers interested in building equitable language technologies by rethinking LLM benchmarks and model architectures.

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.

This corpus contains a collection of texts in the Alsatian dialects which were manually annotated with parts-of-speech, lemmas, translations into French and location entities. The corpus was produced in the context of the RESTAURE project, funded by the French ANR. The current version of the corpus contains 21 documents and 12,907 syntactic words. The annotation process is detailed in the following article: http://hal.archives-ouvertes.fr/hal-01704806 Information about version 3 Version 3 corrects some minor errors in the CONLL-U files: wrong token indexes after multiword tokens and missing _ in glosses. In addition, all files are concatenated into a single CONLL-U file. Information about version 2 Version 2 contains the same annotated documents as version 1, but some errors have been corrected and the annotated corpus is provided in the CoNLL-U format The untokenised and unannotated versions of the documents are found in the "txt" folder. The annotated versions of the documents are found in the "ud" folder (CoNLL-U format). In addition to the form, the lemma and the part-of-speech additional information is also provided: translation of the lemma into French (Gloss field) annotation of location names (NamedType field)

Occitan is a Romance language of France, a little part of Italy and Spain. It includes many written variations, dialectal and spelling variations. Being able to take variation into account is a major challenge to provide the language. Automatic processing of Occitan has been developing over the last ten years. Resources and tools have been developed and are beginning to take dialectal variation into account in these works. However, graphical variation is rarely taken into account. Our research focuses on the automatic annotation into lemmas, parts of speech and verbal inflection of a corpus of texts containing these two types of variation. From this corpus we train robust automatic annotation tools on global variation in Occitan.

Most work on verbalising Knowledge-Graphs (KG) has focused on high-resource languages such as English, Russian, Czech or Arabic. In this paper, we focus on KG-to-Text generation where the output text is in Breton, Irish or Welsh. To overcome the small size of the parallel training data, we combine the strengths of a multilingual encoder-decoder model with denoising fine-tuning on monolingual data and Soft Prompt fine-tuning on a small quantity of KG/text data. We furthermore structure the soft prompt into multiple sub-prompts designed to capture the similarities and differences between English, Knowledge graphs and the three target languages. Our experiments show that our approach outperforms strong baselines and that all sub-prompts contribute to performance.

This paper is a position paper concerning corpus-building strategies in minoritized languages in the Global North. It draws attention to the structure of the non-technical community of speakers, and concretely addresses how their needs can inform the design of technical solutions. Celtic Breton is taken as a case study for its relatively small speaker community, which is rather well-connected to modern technical infrastructures, and is bilingual with a non-English language (French). I report on three different community internal initiatives that have the potential to facilitate the growth of NLP-ready corpora in FAIR practices (Findability, Accessibility, Interoperability, Reusability). These initiatives follow a careful analysis of the Breton NLP situation both inside and outside of academia, and take advantage of preexisting dynamics. They are integrated to the speaking community, both on small and larger scales. They have in common the goal of creating an environment that fosters virtuous circles, in which various actors help each other. It is the interactions between these actors that create qualityenriched corpora usable for NLP, once some low-cost technical solutions are provided. This work aims at providing an estimate of the community’s internal potential to grow its own pool of resources, provided the right NLP resource gathering tools and ecosystem design. Some projects reported here are in the early stages of conception, while others build on decade-long society/research interfaces for the building of resources. All call for feedback from both NLP researchers and the speaking communities, contributing to building bridges and fruitful collaborations between these two groups.

Apertium translation pair for Occitan and French

L'outil VAGO est un système expert de détection du vague lexical qui mesure aussi le degré de subjectivité du discours, ainsi que son niveau de détail. Dans cet article, nous construisons un clone neuronal de VAGO, fondé sur une architecture de type BERT, entraîné à partir des scores du VAGO symbolique sur un corpus de presse française (FreSaDa). L'analyse qualitative et quantitative montre la fidélité de la version neuronale. En exploitant des outils d'explicabilité (LIME), nous montrons ensuite l'intérêt de cette version neuronale d'une part pour l'enrichissement des lexiques de la version symbolique, et d'autre part pour la production de versions dans d'autres langues.

Ce travail présente des contributions récentes à l'effort de doter l'occitan de ressources et outils pour le TAL. Plusieurs ressources existantes ont été modifiées ou adaptées, notamment un tokéniseur à base de règles, un lexique morphosyntaxique et un corpus arboré. Ces ressources ont été utilisées pour entraîner et évaluer des modèles neuronaux pour la lemmatisation. Dans le cadre de ces expériences, un nouveau corpus plus large (2 millions de tokens) provenant du Wikipédia a été annoté en parties du discours, lemmatisé et diffusé.

Apertium translation pair for Breton and French

One of the challenges with finetuning pretrained language models (PLMs) is that their tokenizer is optimized for the language(s) it was pretrained on, but brittle when it comes to previously unseen variations in the data. This can for instance be observed when finetuning PLMs on one language and evaluating them on data in a closely related language variety with no standardized orthography. Despite the high linguistic similarity, tokenization no longer corresponds to meaningful representations of the target data, leading to low performance in, e.g., part-of-speech tagging. In this work, we finetune PLMs on seven languages from three different families and analyze their zero-shot performance on closely related, non-standardized varieties. We consider different measures for the divergence in the tokenization of the source and target data, and the way they can be adjusted by manipulating the tokenization during the finetuning step. Overall, we find that the similarity between the percentage of words that get split into subwords in the source and target data (the split word ratio difference) is the strongest predictor for model performance on target data.

This chapter presents a survey of the current state of technologies for the automatic processing of the French language. It is based on a thorough analysis of existing tools and resources for French, and also provides an accurate presentation of the domain and its main stakeholders (Adda et al. 2022). The chapter documents the presence of French on the internet and describes in broad terms the existing technologies for the French language. It also spells out general conclusions and formulates recommendations for progress towards deep language understanding for French.