Abstract
We propose in this work a new approach that aims to transform a classification corpus into an STS (Semantic Textual Similarity) one. In that sense, we use BERT (Bidirectional Encoder Representations from Transformers) to validate our hypothesis, i.e., a multi-level classification dataset can be converted into an STS dataset which improves the fine-tuning step and evidences the proposed corpus. Also, in our approach, we trained from scratching a BERT model considering the legal texts, called JurisBert which reveals a considered improvement in fastness and precision, and it requires less computational resources than other approaches. JurisBERT uses the concept of sub-language, i.e., a model pre-trained in a language (Brazilian Portuguese) passes through refining (fine-tuning) to better attend to a specific domain, in our case, the legal field. JurisBERT uses 24k pairs of ementas with degrees of similarity varying from 0 to 3. We got this data from search mechanisms available on the court websites to validate the model with real-world data. Our experiments showed JurisBERT is better than other models such as multilingual BERT and BERTimbau with \(3.30\%\) better precision (\(F_1\)), 5 times reduced training time, and using accessible hardware, i.e., low-cost GPGPU architecture. The source code is available at https://github.com/alfaneo-ai/brazilian-legal-text-dataset and the model is here: https://huggingface.co/alfaneo.
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Notes
- 1.
The Conselho Nacional de Justiça is a public institution that aims to help the Brazilian judiciary. It maintains administrative and procedural control and transparency.
- 2.
- 3.
It is an operation that reduces the dimensionality of data by applying an aggregation of type max average.
- 4.
It is a dense vector of floating points that aims to capture the semantic of the text in the vector space.
- 5.
The súmulas summarizes the dominant precedent of a given court.
- 6.
Proposed dataset and web scrappers are available here: https://github.com/alfaneo-ai/brazilian-legal-text-dataset, and the models, here: https://huggingface.co/alfaneo.
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Acknowledgments
We thank the support of the UFMS (Universidade Federal de Mato Grosso do Sul), FUNDECT, and Finep. We also thank the support of the INCT of the Future Internet for Smart Cities funded by CNPq, proc. 465446/2014-0, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, and FAPESP, proc. 2014/50937-1 and 2015/24485-9.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of FUNDECT, Finep, FAPESP, CAPES, and CNPq.
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Viegas, C.F.O., Costa, B.C., Ishii, R.P. (2023). JurisBERT: A New Approach that Converts a Classification Corpus into an STS One. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_24
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