BERTabaporu: Assessing a Genre-specific Language Model for Portuguese NLP

,; da Costa, Pablo; Pavan, Matheus; ,; dos Santos, Wesley; ,; da Silva, Samuel; ,; Paraboni, Ivandré; ,

doi:10.26615/978-954-452-092-2_024

Cited by 6 publications

References 21 publications

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ODSBAHIA-PTBR: A Natural Language Processing Model to Support Sustainable Development Goals

Santos,

Moraes

2024

RGSA

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Objective: The present study aims to propose an approach for the objective classification of texts in Portuguese in relation to the Sustainable Development Goals (SDGs) of Brazil's 2030 Agenda. Theoretical Framework: The study uses natural language processing (NLP) techniques with deep learning, using pre-trained models such as BERTimbau Base, DeBERTinha and Albertina. In addition, it considers the existing gaps in the literature regarding the classification of texts in Portuguese related to the 17 UN SDGs and also including three new SDGs proposed in the document Guide Agenda 2030: Integrating SDGs, Education and Society, prepared in 2020 in partnership between UnB and UNESP, SDGs 18 (Ethnic-Racial Equality), 19 (Art, Culture and Communication) and 20 (Rights of Indigenous Peoples and Traditional Communities). Method: La investigación es exploratoria, descriptiva y aplicada, con enfoque cuantitativo y procedimientos experimentales. Los modelos previamente entrenados se ajustaron al conjunto de datos de etiquetas múltiples creado específicamente para la tarea. La Base BERTimbau presentó el mejor rendimiento y se utilizó como base para la creación del modelo ODSBahia-PTBR, evaluado con métricas como precisión (82%), recuerdo (72%) y F1-Score (77%). Results and Discussion: El ODSBahia-PTBR logró una precisión del 95% al traducir y clasificar el conjunto de datos OSDG. Los resultados ponen de manifiesto la efectividad del modelo en la identificación y categorización de textos alineados con los ODS, siendo especialmente relevante para el seguimiento de las interseccionalidades entre los ODS propuestos. Research Implications: The SDGbahia-PTBR model has practical implications by offering an innovative tool for different stakeholders to monitor and analyze initiatives aligned with the SDGs, contributing to the evaluation and promotion of the 2030 Agenda. Originality/Value: This research is a pioneer in including SDGs 18, 19 and 20 in Portuguese-language text classifiers, offering an unprecedented and applicable approach to sustainable monitoring in Brazil and other Portuguese-speaking countries.

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ODSBAHIA-PTBR: A Natural Language Processing Model to Support Sustainable Development Goals

Santos,

Moraes

2024

RGSA

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Prompt-based mental health screening from social media text

Santos,

Paraboni

2024

Anais Do XIII Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2024)

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This article presents a method for prompt-based mental health screening from a large and noisy dataset of social media text. Our method uses GPT 3.5. prompting to distinguish publications that may be more relevant to the task, and then uses a straightforward bag-of-words text classifier to predict actual user labels. Results are found to be on pair with a BERT mixture of experts classifier, and incurring only a fraction of its training costs.

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A benchmark for Portuguese zero-shot stance detection

Pavan,

Paraboni

2024

JBCS

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Stance detection is the task of inferring for/against attitudes towards a particular target from text. As targets are in principle unlimited, however, research in the field has moved from so-called in-domain classification (which assume the availability of a sufficient number of stances towards the intended target for training purposes) to more realistic zero-shot scenarios. However, regardless of which - or how much - training data is taken into account, most existing zero-shot approaches are devoted to the English language, in stark opposition to alternatives devoted to Portuguese. As a means to overcome some of these difficulties, this article presents a benchmark (hereby understood as the combination of a dataset, baseline systems and their results) for zero-shot Portuguese stance detection that is, to the best of our knowledge, the first of it kind. More specifically, we adapt a number of existing models available for the English language to Portuguese, and introduce novel approaches to the task based on more recent prompt engineering methods and off-task labelling, achieving SOTA results that are, in some cases, even superior to in-domain classification.

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BERTabaporu: Assessing a Genre-specific Language Model for Portuguese NLP

Cited by 6 publications

References 21 publications

ODSBAHIA-PTBR: A Natural Language Processing Model to Support Sustainable Development Goals

ODSBAHIA-PTBR: A Natural Language Processing Model to Support Sustainable Development Goals

Prompt-based mental health screening from social media text

A benchmark for Portuguese zero-shot stance detection

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