Brazil

BRACIS 2021

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2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

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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). <br>**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. <br>**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**.<br><br><br>

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

A chatbot is an artificial intelligence based system aimed at chatting with users, commonly used as a virtual assistant to help people or answer questions. Intent classification is an essential task for chatbots where it aims to identify what the user wants in a certain dialogue. However, for many domains, little data are available to properly train those systems. In this work, we evaluate the performance of two methods to generate synthetic data for chatbots, one based on template questions and another based on neural text generation. We build four datasets that are used training chatbot components in the intent classification task. We intend to simulate the task of migrating a search-based portal to an interactive dialogue-based information service by using artificial datasets for initial model training. Our results show that template-based datasets are slightly superior to those neural-based generated in our application domain, however, neural-generated present good results and they are a viable option when one has limited access to domain experts to hand-code text templates.

Evaluation of Synthetic Datasets Generation for Intent Classification Tasks in Portuguese

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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Evaluation of Synthetic Datasets Generation for Intent Classification Tasks in Portuguese

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