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        <title>Data-to-Text Generation with Iterative Text Editing</title>
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        <namePart type="given">Zdeněk</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Data-to-Text Generation with Iterative Text Editing
%A Kasner, Zdeněk
%A Dušek, Ondřej
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kasner-dusek-2020-data
%X 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.
%R 10.18653/v1/2020.inlg-1.9
%U https://aclanthology.org/2020.inlg-1.9
%U https://doi.org/10.18653/v1/2020.inlg-1.9
%P 60-67

Markdown (Informal)

[Data-to-Text Generation with Iterative Text Editing](https://aclanthology.org/2020.inlg-1.9) (Kasner & Dušek, INLG 2020)

• Data-to-Text Generation with Iterative Text Editing (Kasner & Dušek, INLG 2020)

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.