Search and Learn: Improving Semantic Coverage for Data-to-Text Generation

• URL: http://arxiv.org/abs/2112.02770v1
• Date: Mon, 6 Dec 2021 03:51:56 GMT
• Title: Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
• Authors: Shailza Jolly, Zi Xuan Zhang, Andreas Dengel, Lili Mou
• Abstract summary: In this work, we focus on few-shot data-to-text generation. We propose a search-and-learning approach that leverages pretrained language models but inserts the missing slots to improve semantic coverage. Experiments show that our model achieves high performance on E2E and WikiBio datasets.
• Score: 30.07712039293558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-to-text generation systems aim to generate text descriptions based on input data (often represented in the tabular form). A typical system uses huge training samples for learning the correspondence between tables and texts. However, large training sets are expensive to obtain, limiting the applicability of these approaches in real-world scenarios. In this work, we focus on few-shot data-to-text generation. We observe that, while fine-tuned pretrained language models may generate plausible sentences, they suffer from the low semantic coverage problem in the few-shot setting. In other words, important input slots tend to be missing in the generated text. To this end, we propose a search-and-learning approach that leverages pretrained language models but inserts the missing slots to improve the semantic coverage. We further fine-tune our system based on the search results to smooth out the search noise, yielding better-quality text and improving inference efficiency to a large extent. Experiments show that our model achieves high performance on E2E and WikiBio datasets. Especially, we cover 98.35% of input slots on E2E, largely alleviating the low coverage problem.

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