  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 proposed with an end-to-end deep learning
flavor, which handles non-linguistic input in natural language without explicit
intermediary representations. This study compares the most often employed
methods for generating Brazilian Portuguese texts from structured data. Results
suggest that explicit intermediate steps in the generation process produce
better texts than the ones generated by neural end-to-end architectures,
avoiding data hallucination while better generalizing to unseen inputs. Code
and corpus are publicly available.
