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

Amit Moryossef, Yoav Goldberg, Ido Dagan
2019 Proceedings of the 12th International Conference on Natural Language Generation  
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
more » ... 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.
doi:10.18653/v1/w19-8645 dblp:conf/inlg/MoryossefGD19 fatcat:7gudhkre2vc4dj5hfe74zyvasa