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Despite the success of the Universal Dependencies (UD) project exemplified by its impressive language breadth, there is still a lack in `within-language breadth': most treebanks focus on standard languages. Even for German, the language with the most annotations in UD, so far no treebank exists for one of its language varieties spoken by over 10M people: Bavarian. To contribute to closing this gap, we present the first multi-dialect Bavarian treebank (MaiBaam) manually annotated with part-of-speech and syntactic dependency information in UD, covering multiple text genres (wiki, fiction, grammar examples, social, non-fiction). We highlight the morphosyntactic differences between the closely-related Bavarian and German and showcase the rich variability of speakers' orthographies. Our corpus includes 15k tokens, covering dialects from all Bavarian-speaking areas spanning three countries. We provide baseline parsing and POS tagging results, which are lower than results obtained on German and vary substantially between different graph-based parsers. To support further research on Bavarian syntax, we make our dataset, language-specific guidelines and code publicly available.

We investigate the effect of integrating lexicon information to an extremely low-resource language when annotated data is scarce for morpho-syntactic analysis. Obtaining such data and linguistic resources for these languages are usually constrained by a lack of human and financial resources making this task particularly challenging. In this paper, we describe the collection and leverage of a bilingual lexicon for Poitevin-Saintongeais, a regional language of France, to create augmented data through a neighbor-based distributional method. We assess this lexicon-driven approach in improving POS tagging while using different lexicon and augmented data sizes. To evaluate this strategy, we compare two distinct paradigms: neural networks, which typically require extensive data, and a conventional probabilistic approach, in which a lexicon is instrumental in its performance. Our findings reveal that the lexicon is a valuable asset for all models, but in particular for neural, demonstrating an enhanced generalization across diverse classes without requiring an extensive lexicon size.

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.

In this position paper we argue that researchers interested in language and/or language technologies should attend to challenges of linguistic and algorithmic injustice together with language communities. We put forward that this can be done by drawing together diverse scholarly and experiential insights, building strong interdisciplinary teams, and paying close attention to the wider social, cultural and historical contexts of both language communities and the technologies we aim to develop.

In this paper we present a series of experiments towards POS tagging Corsican, a less-resourced language spoken in Corsica and linguistically related to Italian. The first contribution is Corsican-POS, the first gold standard POS-tagged corpus for Corsica, composed of 500 sentences manually annotated with the Universal POS tagset. Our second contribution is a set of experiments and evaluation of POS tagging models which starts with a baseline model for Italian and is aimed at finding the best training configuration, namely in terms of the size and combination strategy of the existing raw and annotated resources. These experiments result in (i) the first POS tagger for Corsican, reaching an accuracy of 93.38%, (ii) a quantification of the gain provided by the use of each available resource. We find that the optimal configuration uses Italian word embeddings further specialized with Corsican embeddings and trained on the largest gold corpus for Corsican available so far.

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.

This paper presents a first attempt to apply Universal Dependencies (De Marneffe et al., 2021) to train a parser for Mauritian Creole (MC), a French-based Creole language spoken on the island of Mauritius. This paper demonstrates the construction of a 161-sentence (1007-token) treebank for MC and evaluates the performance of a part-of-speech tagger and Universal Dependencies parser trained on this data. The sentences were collected from publicly available grammar books (Syea, 2013) and online resources (Baker and Kriegel, 2013), as well as from government-produced school textbooks (Antonio-Françoise et al., 2021; Natchoo et al., 2017). The parser, trained with UDPipe 2 (Straka, 2018), reached F1 scores of UPOS=86.2, UAS=80.8 and LAS=69.8. This fares favorably when compared to models of similar size for other under-resourced Indigenous and Creole languages. We then address some of the challenges faced when applying UD to Creole languages in general and to Mauritian Creole in particular. The main challenge was the handling of spelling variation in the input. Other issues include the tagging of modal verbs, middle voice sentences, and parts of the tense-aspect-mood system (such as the particle fek).

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.

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.