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Traditionally, most data-to-text applications have been designed using amodular pipeline architecture, in which non-linguistic input data is convertedinto natural language through several intermediate transformations. Incontrast, recent neural models for data-to-text generation have been proposedas end-to-end approaches, where the non-linguistic input is rendered in naturallanguage with much less explicit intermediate representations in-between. Thisstudy introduces a systematic comparison between neural pipeline and end-to-enddata-to-text approaches for the generation of text from RDF triples. Botharchitectures were implemented making use of state-of-the art deep learningmethods as the encoder-decoder Gated-Recurrent Units (GRU) and Transformer.Automatic and human evaluations together with a qualitative analysis suggestthat having explicit intermediate steps in the generation process results inbetter texts than the ones generated by end-to-end approaches. Moreover, thepipeline models generalize better to unseen inputs. Data and code are publiclyavailable.

Neural data-to-text generation: A comparison between pipeline and end-to-end architectures