Data-to-Text Generation with Iterative Text Editing

• URL: http://arxiv.org/abs/2011.01694v2
• Date: Thu, 28 Jan 2021 14:30:14 GMT
• Title: Data-to-Text Generation with Iterative Text Editing
• Authors: Zden\v{e}k Kasner and Ond\v{r}ej Du\v{s}ek
• Abstract summary: We present a novel approach to data-to-text generation based on iterative text editing. 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 and reranked with an off-the-shelf pre-trained language model.
• Score: 3.42658286826597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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