7 citations · 18 across the 7 of their papers we have counts for
6 papers · 1 filter
Question Answering with Texts and Tables through Deep Reinforcement Learning
Marcos M. José, Flávio N. Cação, Maria F. Ribeiro +3
This paper proposes a novel architecture to generate multi-hop answers to open domain questions that require information from texts and tables, using the Open Table-and-Text Questi…
Assessing Logical Reasoning Capabilities of Encoder-Only Transformer Models
Paulo Pirozelli, Marcos M. José, Paulo de Tarso P. Filho +2
Logical reasoning is central to complex human activities, such as thinking, debating, and planning; it is also a central component of many AI systems as well. In this paper, we inv…
Benchmarks for Pirá 2.0, a Reading Comprehension Dataset about the Ocean, the Brazilian Coast, and Climate Change
Paulo Pirozelli, Marcos M. José, Igor Silveira +5
Pirá is a reading comprehension dataset focused on the ocean, the Brazilian coast, and climate change, built from a collection of scientific abstracts and reports on these topics.…
Comparing Computational Architectures for Automated Journalism
Yan V. Sym, João Gabriel M. Campos, Marcos M. José +1
The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for data-to-text generation have been propos…
Integrating question answering and text-to-SQL in Portuguese
Marcos Menon José, Marcelo Archanjo José, Denis Deratani Mauá +1
Deep learning transformers have drastically improved systems that automatically answer questions in natural language. However, different questions demand different answering techni…
DEEPAGÉ: Answering Questions in Portuguese about the Brazilian Environment
Flávio Nakasato Cação, Marcos Menon José, André Seidel Oliveira +3
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…