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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. By 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 systematicdoi:10.18653/v1/d19-1052 dblp:conf/emnlp/FerreiraLMK19 fatcat:63otuy4hljfkbccauxbru2wgom