Data-to-Text Generation with Iterative Text Editing
Zdeněk Kasner, Ondřej Dušek · 2020
We present a novel approach to data-to-text generation based on iterative text editing.Our approach maximizes the completeness and semantic accuracy of the output text while leveraging the abilities of recent pre-trained models for text editing (LASERTAGGER) and language modeling (GPT-2) to improve the text fluency.To this end, we first transform data items to text using trivial templates, and then we iteratively improve the resulting text by a neural model trained for the sentence fusion task.The output of the model is filtered by a simple heuristic and reranked with an offthe-shelf pre-trained language model.We evaluate our approach on two major data-to-text datasets (WebNLG, Cleaned E2E) and analyze its caveats and benefits.Furthermore, we show that our formulation of data-to-text generation opens up the possibility for zero-shot domain adaptation using a general-domain dataset for sentence fusion.