Brazil

BRACIS 2021

Are Foundation Models the Future of AI?

Welcome to Enago See the Future 2021 (Global). To access the complimentary content,[ please register here](https://www.enago.com/see-the-future/conference-2021/register/).

https://www.enago.com/see-the-future/conference-2021/register/

Enago See the Future 2021 (Global - English)

Research publication never slowed down while the Earth stood still as COVID crisscrossed international borders in multiple waves. The numbers of submissions and publications continuously grew while most productions of modern societies dwindled. At the same time, the long-foretold paradigm shift has happened before our eyes. Modes of publication and research collaboration have undergone rapid fundamental changes. In these uncertain times, change and greater openness remain the only constant. See the Future 2021 Virtual Conference will focus on these vital aspects of research publication and once again challenge our preconceptions. 

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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**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

[Register now](http://c4ai.inova.usp.br/bracis2021/)

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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

Neural machine translation (NMT) nowadays requires an increasing
amount of data and computational power, so succeeding in this task with limited
data and using a single GPU might be challenging. Strategies such as the use
of pre-trained word embeddings, subword embeddings, and data augmentation
solutions can potentially address some issues faced in low-resource experimental settings, but their impact on the quality of translations is unclear. This work
evaluates some of these strategies on two low-resource experiments beyond just
reporting BLEU: errors are categorized on the Portuguese-English pair with the
help of a translator, considering semantic and syntactic aspects. The BPE subword approach has shown to be the most effective solution, allowing a BLEU
increase of 59% p.p. compared to the standard Transformer.

Tackling neural machine translation in low-resource settings: a Portuguese case study

The advance on Question Answering systems has achieved significant results, and new related problems have arisen. Few studies have addressed the Complex Knowledge Base Question Answering (C-KBQA) problem. This work presents an overview of C-KBQA systems. A collection of 54 papers were systematically selected, and a map of the methods, approaches, trends, future directions, and gaps in the C-KBQA research was performed. This study shows that C-KBQA systems need to handle multi-hop and constraint questions, and two approaches are usually used to address this problem.

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Tackling neural machine translation in low-resource settings: a Portuguese case study

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