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
Editors:
Brian Davis, Yvette Graham, John Kelleher, Yaji Sripada
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
60–67
Language:
URL:
https://aclanthology.org/2020.inlg-1.9
DOI:
10.18653/v1/2020.inlg-1.9
Bibkey:
Cite (ACL):
Zdeněk Kasner and Ondřej Dušek. 2020. Data-to-Text Generation with Iterative Text Editing. In Proceedings of the 13th International Conference on Natural Language Generation, pages 60–67, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Data-to-Text Generation with Iterative Text Editing (Kasner & Dušek, INLG 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.inlg-1.9.pdf
Supplementary attachment:
 2020.inlg-1.9.Supplementary_Attachment.pdf
Code
 kasnerz/d2t_iterative_editing