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Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation

2019-09-22 11:41:53
Amit Moryossef, Ido Dagan, Yoav Goldberg

Abstract

We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component that can generate effective plans several orders of magnitude faster than the original planner; (2) we incorporate typing hints that improve the model's ability to deal with unseen relations and entities; (3) we introduce a verification-by-reranking stage that substantially improves the faithfulness of the resulting texts; (4) we incorporate a simple but effective referring expression generation module. These extensions result in a generation process that is faster, more fluent, and more accurate.

Abstract (translated)

URL

https://arxiv.org/abs/1909.09986

PDF

https://arxiv.org/pdf/1909.09986.pdf


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