Data-to-Text Generation with Iterative Text Editing

Zdeněk Kasner, Ondřej Dušek


Abstract
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 off-the-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.
Anthology ID:
2020.inlg-1.9
Volume:
Proceedings of the 13th International Conference on Natural Language Generation
Month:
December
Year:
2020
Address:
Dublin, Ireland
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–67
Language:
URL:
https://www.aclweb.org/anthology/2020.inlg-1.9
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.inlg-1.9.pdf
Supplementary attachment:
 2020.inlg-1.9.Supplementary_Attachment.pdf