  Traditionally, most data-to-text applications have been designed using a
modular pipeline architecture, in which non-linguistic input data is converted
into natural language through several intermediate transformations. In
contrast, recent neural models for data-to-text generation have been proposed
as end-to-end approaches, where the non-linguistic input is rendered in natural
language with much less explicit intermediate representations in-between. This
study introduces a systematic comparison between neural pipeline and end-to-end
data-to-text approaches for the generation of text from RDF triples. Both
architectures were implemented making use of state-of-the art deep learning
methods as the encoder-decoder Gated-Recurrent Units (GRU) and Transformer.
Automatic and human evaluations together with a qualitative analysis suggest
that having explicit intermediate steps in the generation process results in
better texts than the ones generated by end-to-end approaches. Moreover, the
pipeline models generalize better to unseen inputs. Data and code are publicly
available.
