This paper investigates morphosyntactic covariation in Brazilian Portuguese (BP) to assess whether dialectal origin can be inferred from the combined behavior of linguistic variables. Focusing on four grammatical phenomena related to pronouns, correlation and clustering methods are applied to model covariation and dialectal distribution. The results indicate that correlation captures only limited pairwise associations, whereas clustering reveals speaker groupings that reflect regional dialectal patterns. Despite the methodological constraints imposed by differences in sample size requirements between sociolinguistics and computational approaches, the study highlights the importance of interdisciplinary research. Developing fair and inclusive language technologies that respect dialectal diversity outweighs the challenges of integrating these fields.
Figures & tables
Figure 1: Scherre et al. dialectal distribution of 2P in Brazil, Scherre et al. (2015)
Table 2: Linear combinations of second-person subject, clitic, determiner and possessive in Brazilian Portuguese
Displacement Type
Description
Displacement 1
UFS students born in Aracaju’s Metropolitan region (Aracaju, Nossa Senhora do Socorro, São Cristóvão, and Barra dos Coqueiros cities) and who reside there
Displacement 2
UFS students born in the countryside of Sergipe who make the daily commute to UFS
Displacement 3
UFS students born in the countryside of Sergipe who reside in Aracaju’s Metropolitan region
Displacement 4
UFS students born in other states (predominantly Bahia and Alagoas) who currently reside in Aracaju’s Metropolitan region
Table 3: Control of displacements and dialectal boundaries
Pronouns te and lhe preceding verbs in proclitic position. Pronouns te and lhe following verbs in enclitic position. Pronouns te and lhe following verbs in enclitic position, connected by a punctuation mark (-).
Regional accent classification in Brazilian Portuguese (pt-BR) suffers from the need for reliable labeling. While large self-supervised learning (SSL) speech models are powerful, their training pipelines dilute sociophonetic information, since accent labels are generally not reliable or are not used in training objectives. This work introduces a novel workflow for feature extraction using only acoustic labels. By isolating explicit regional accent landmarks and using a phoneme-based forced aligner (ZIPA), our targeted feature set captures dialectal variance more effectively than utterance embeddings, demonstrating that localized features can outperform general-purpose architectures on accent-related tasks using minimal and objective data labels.
Pedro H. L. Leite, Pedro Benevenuto Valadares, Luiz W. P. Biscainho
PEE/COPPE, UFRJ, Rio de Janeiro-RJ · Faculdade de Engenharia Elétrica e Computação (FEEC), UNICAMP, Campinas-SP · DEL/Poli & PEE/COPPE, UFRJ, Rio de Janeiro-RJ
Recent research on dialectal NLP has identified data scarcity as a primary limitation. To address this limitation, this paper presents a catalog of contemporary Basque dialectal data and resources, offering a systematic and comprehensive compilation of the dialectal data currently available in Basque. Two types of data sources have been distinguished: online data originally written in some dialect, and standard-to-dialect adapted data. The former includes all dialectal data that can be found online, such as news and radio sites, informal tweets, as well as online resources such as dictionaries, atlases, grammar rules, or videos. The latter consists of data that has been adapted from the standard variety to dialectal varieties, either manually or automatically. Regarding the manual adaptation, the test split of the XNLI Natural Language Inference dataset was manually adapted into three Basque dialects: Western, Central, and Navarrese-Lapurdian, yielding a high-quality parallel gold standard evaluation dataset. With respect to the automatic dialectal adaptation, the automatically adapted physical commonsense dataset (BasPhyCowest) underwent additional manual evaluation by native speakers to assess its quality and determine whether it could serve as a viable substitute for full manual adaptation (i.e., silver data creation).
As Large Language Models (LLMs) become embedded in everyday communication, capturing regional linguistic variation is essential for reliable and equitable language use. In Portuguese, European (pt-PT) and Brazilian (pt-BR) varieties remain unevenly represented, with pt-BR dominating in data quantity, while LLM preference for Portuguese variants remains underexplored. To address this gap, we introduce P3B3, an expert-curated language variety agnostic benchmark of conversational prompts, along with an evaluation framework for measuring variety bias and controllability. Experiments on several models show that most LLMs exhibit a strong bias toward pt-BR, with variation in controllability across models. These results highlight the need for more balanced multilingual representation across language varieties.