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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.

Machine translation has been researched using deep neural networks in recent years. These networks require lots of data to learn abstract representations of the input stored in continuous vectors. Dialect translation has become more important since the advent of social media. In particular, when dialect speakers and standard language speakers no longer understand each other, machine translation is of rising concern. Usually, dialect translation is a typical low-resourced language setting facing data scarcity problems. Additionally, spelling inconsistencies due to varying pronunciations and the lack of spelling rules complicate translation. This paper presents the best-performing approaches to handle these problems for Alemannic dialects. The results show that back-translation and conditioning on dialectal manifestations achieve the most remarkable enhancement over the baseline. Using back-translation, a significant gain of +4.5 over the strong transformer baseline of 37.3 BLEU points is accomplished. Differentiating between several Alemannic dialects instead of treating Alemannic as one dialect leads to substantial improvements: Multi-dialectal translation surpasses the baseline on the dialectal test sets. However, training individual models outperforms the multi-dialectal approach. There, improvements range from 7.5 to 10.6 BLEU points over the baseline depending on the dialect.

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.

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 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).

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.

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.

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.

Occitan is a minority language spoken in Southern France, some Alpine Valleys of Italy, and the Val d'Aran in Spain, which only very recently started developing language and speech technologies. This paper describes the first project for designing a Text-to-Speech synthesis system for one of its main regional varieties, namely Gascon. We used a state-of-the-art deep neural network approach, the Tacotron2-WaveGlow system. However, we faced two additional difficulties or challenges: on the one hand, we wanted to test if it was possible to obtain good quality results with fewer recording hours than is usually reported for such systems; on the other hand, we needed to achieve a standard, non-Occitan pronunciation of French proper names, therefore we needed to record French words and test phoneme-based approaches. The evaluation carried out over the various developed systems and approaches shows promising results with near production-ready quality. It has also allowed us to detect the phenomena for which some flaws or fall of quality occur, pointing at the direction of future work to improve the quality of the actual system and for new systems for other language varieties and voices.

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.

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.

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.

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.