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ARBRES is an ongoing project of open science implemented as a platform (“wikigrammar”) documenting both the Breton language itself and the state of research and engineering work in linguistics and NLP. Along its nearly 15 years of operation, it has aggregated a wealth of linguistic data in the form of interlinear glosses with translations illustrating lexical items, grammatical features, dialectal variations… While these glosses were primarily meant for human consumption, their volume and the regular format imposed by the wiki engine used for the website also make them suitable for machine processing. ARBRES Kenstur is a new parallel corpus derived from the glosses in ARBRES, including about 5k phrases and sentences in Breton along with translations in standard French. The nature of the original data — sourced from field linguistic inquiries meant to document the structure of Breton — leads to a resource that is mechanically more concerned with the internal variations of the language and rare phenomena than typical parallel corpora. Preliminaries experiments in using this corpus show that it can help improve machine translation for Breton, demonstrating that sourcing data from field linguistic documentation can be a way to help provide NLP tools for minority and low-resource languages.

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.

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)

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.

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.

This paper presents OcWikiDisc, a new freely available corpus in Occitan, as well as language identification experiments on Occitan done as part of the corpus building process. Occitan is a regional language spoken mainly in the south of France and in parts of Spain and Italy. It exhibits rich diatopic variation, it is not standardized, and it is still low-resourced, especially when it comes to large downloadable corpora. We introduce OcWikiDisc, a corpus extracted from the talk pages associated with the Occitan Wikipedia. The version of the corpus with the most restrictive language filtering contains 8K user messages for a total of 618K tokens. The language filtering is performed based on language identification experiments with five off-the-shelf tools, including the new fasttext's language identification model from Meta AI's No Language Left Behind initiative, released in July 2022.

This paper outlines the ongoing effort of creating the first treebank for Occitan, a low-ressourced regional language spoken mainly in the south of France. We briefly present the global context of the project and report on its current status. We adopt the Universal Dependencies framework for this project. Our methodology is based on two main principles. Firstly, in order to guarantee the annotation quality, we use the agile annotation approach. Secondly, we rely on pre-processing using existing tools (taggers and parsers) to facilitate the work of human annotators, mainly through a delexicalized cross-lingual parsing approach. We present the results available at this point (annotation guidelines and a sub-corpus annotated with PoS tags and lemmas) and give the timeline for the rest of the work.

This article describes the creation of corpora with part-of-speech annotations for three regional languages of France: Alsatian, Occitan and Picard. These manual annotations were performed in the context of the RESTAURE project, whose goal is to develop resources and tools for these under-resourced French regional languages. The article presents the tagsets used in the annotation process as well as the resulting annotated corpora.

We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.