Brazil

Subjects change frequently in moderated debates with several participants,
such as in parliamentary sessions, electoral debates, and trials. Partitioning a debate into blocks with the same subject is essential for understanding. Often a moderator is responsible for defining when a new block begins so that the task of automatically partitioning a moderated debate can focus solely on the moderator&#39;s behavior. 
In this paper, we (i) propose a new algorithm, DEBACER, which partitions moderated debates; (ii) carry out a comparative study between conventional and BERTimbau pipelines; and (iii) validate DEBACER applying it to the minutes of the Assembly of the Republic of Portugal. Our results show the effectiveness of DEBACER.

BRACIS 2021

DEBACER: a method for slicing moderated debates

dialogue partitioning

speech split

spoken text processing

political documents

natural language processing

**BRACIS** originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – **SBIA** (22 editions), and the Brazilian Symposium on Neural Networks – **SBRN** (13 editions). **ENIAC** is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

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BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (22 editions), and the Brazilian Symposium on Neural Networks – SBRN (13 editions). 
ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

Subjects change frequently in moderated debates with several participants,
such as in parliamentary sessions, electoral debates, and trials. Partitioning a debate into blocks with the same subject is essential for understanding. Often a moderator is responsible for defining when a new block begins so that the task of automatically partitioning a moderated debate can focus solely on the moderator's behavior. 
In this paper, we (i) propose a new algorithm, DEBACER, which partitions moderated debates; (ii) carry out a comparative study between conventional and BERTimbau pipelines; and (iii) validate DEBACER applying it to the minutes of the Assembly of the Republic of Portugal. Our results show the effectiveness of DEBACER.

technical paper

The challenge of climate change and biome conservation is one of the most 
pressing issues of our time -- particularly in Brazil, where key environmental reserves are located. Given the availability of large textual databases on ecological themes, it is natural to resort to question answering (QA) systems to increase social awareness and understanding about these topics. In this work, we introduce multiple QA systems that combine in novel ways the BM25 algorithm, a sparse retrieval technique, with PTT5, a pre-trained state-of-the-art language model. Our QA systems focus on the Portuguese language, thus offering resources not found elsewhere in the literature. As training data, we collected questions from open-domain datasets, as well as content from the Portuguese Wikipedia and news from the press. We thus contribute with innovative architectures and novel applications, attaining an F1-score of 36.2 with our best model.

DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment

The translation of natural language questions to SQL queries has attracted growing attention, in particular in connection with transformers and similar language models. A large number of techniques are geared towards the English language; in this work, we thus investigated translation to SQL when input questions are given in the Portuguese language. To do so, we properly adapted state-of-the-art tools and resources. We changed the RAT-SQL+GAP system by relying on a multilingual BART model (we report tests with other language models), and we produced a translated version of the Spider dataset. Our experiments expose interesting phenomena that arise when non-English languages are targeted; in particular, it is better to train with original and translated training datasets together, even if a single target language is desired. This multilingual BART model fine-tuned with a double-size training dataset (English and Portuguese) achieved 83\% of the baseline, making inferences for the Portuguese test dataset. This investigation can help other researchers to produce results in Machine Learning in a language different from English. Our multilingual ready version of RAT-SQL+GAP and the data are available, open-sourced as mRAT-SQL+GAP at: https://github.com/C4AI/gap-text2sql

mRAT-SQL+GAP: A Portuguese Text-to-SQL Transformer

As one of the most primitive pattern organization tasks, clustering is a hard grouping problem in exploratory data analysis. Most standard clustering algorithms are easily trapped into local minima points from the problem search space, once such models lack good global search capabilities. In this work, a memetic Swarm Intelligence (SIs) algorithm is presented, based on Group Search Optimization and K-Means, called MGSO, that attempts both finding the best number of final clusters and the best distribution among patterns in clusters, simultaneously. The proposed MGSO showed to be able to find good global solutions through a testing bed of nine real-world data sets, in comparison to other SIs and Evolutionary Algorithms from the literature.

Uma Abordagem de Agrupamento Automático de Dados Baseada na Otimização por Busca em Grupo Memética

Os Sistemas de Multi-Classificadores (MCSs) constituem um dos paradigmas mais competitivos para a obtenção de classificações precisas no campo do aprendizado de máquina. Este artigo busca avaliar se a utilização de algoritmos híbridos de enxames pode melhorar a performance dos MCSs por meio da otimização de pesos em combinações por voto majoritário ponderado. A metodologia proposta rendeu resultados competitivos em 25 conjuntos de dados de referência. Adotou-se a acurácia como função objetivo a ser maximizada pelas seguintes meta-heurísticas: otimização do exame de partículas (PSO), a colônia artificial de abelhas (ABC), e a alternativa híbrida das anteriores usando a técnica de multi enxames dinâmicos (DM-PSO-ABC).

Hybrid Swarm Enhanced Classifier Ensembles

Este trabalho descreve a exploração dos efeitos de adicionar ergodicidade a um Algoritmo Evolucionário com inspiração Quântica (QiEA) utilizando sequências caóticas em dois operadores: crossover uniforme caótico e porta quântica de atualização caótica. O algoritmo é aplicado na seleção de atributos de uma aplicação de interface cérebro-computador (BCI) que emprega o eletroencefalograma (EEG) da imaginação do movimento das mãos direita ou esquerda. Os resultados são comparados com os de um QiEA e Algoritmo Genético (GA) tradicionais, demonstrando que o QiEA caótico pode aperfeiçoar significativamente o tempo de convergência do modelo com só uma pequena perda na acurácia final do modelo.

Algoritmo Evolucionário com Inspiração Quântica e Sequências Caóticas para Seleção de Atributos em Interfaces Cérebro-Computador

Esta apresentação demonstra uma nova metodologia de incorporação de preferências em problemas multiobjetivo através da definição de uma região de interesse na forma de um hiper-elipsoide. A adição dessa camada ao algoritmo de otimização visa auxiliar a tomada de decisão, selecionando soluções de acordo com as preferências definidas pelo tomador de decisão. Além disso, a forma hiper-elipsoide permite que o usuário altere a quantidade de elementos selecionados ao se modificar o foco do hiper-elipsoide, ampliando ou reduzindo a área de cobertura da fronteira Pareto.

Definição de regiões de interesse em problemas multiobjetivo utilizando hiper-elipses

Um conjunto de estratégias automatizadas de negociação em mercados de capitais pode ser disposto em um portfólio buscando maximização de rendimentos e minimização de perdas. A melhor combinação para o portfólio requer a atribuição de pesos ótimos para cada estratégia, considerando diferentes indicadores financeiros. Neste artigo é proposta a aplicação de um algoritmo evolutivo com abordagem lexicográfica para otimizar um portfólio de estratégias automatizadas aplicadas ao mercado futuro brasileiro. Os experimentos consideram diferentes funções-objetivo de indicadores financeiros, com diferentes ordenações, além de condições de otimização e de variações temporais, aplicando dados históricos de minicontratos do índice futuro do Ibovespa e do dólar.

Otimização evolutiva lexicográfica de um portfólio de estratégias automatizadas no mercado futuro brasileiro

Emotions are one of the pillars of human communication, especially spoken language. In emotional speech, they can be identified by inherent attributes of the voice, such as pitch, frequency, intensity etc. In this paper, a model based on artificial data augmentation and Deep Learning, more specifically a Convolutional Recurrent Neural Network, was proposed to automate this emotion identification task by being trained on the RAVDESS database with cross-validation technique. Evaluated by the accuracy and F1-Score metrics, the model achieved in them 76.25% and 76% on average and a maximum of 83.33% and 80%, respectively, which are slightly better results than those presented in related research.

Identification of Emotions in Spoken Language Using Deep Learning

In Brazil, citizens can report irregularities in the Public Administration. The classification of these complaints needs information that is not in their texts. this article aims is to propose a methodology for extracting and enriching information identified in complaints texts. This methodology provides as output a set of structured data capable of characterizing the complaints. To validate the proposal, a case study was done. The study showed that structured data use enabled an improvement in the performance of the complaint’s classification.

Extraction and enrichment of features to improve complaint text classification performance

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware.
In particular, we study the impact over a dataset with few examples and one that is being constructed.
We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM).
Our experiments show that deep learning algorithms have a performance advantage over classical techniques.
However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources.
Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost.
The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

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DEBACER: a method for slicing moderated debates

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DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment

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