This is the codebase for the ACL-20 paper Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation
Some codes are borrowed from OpenNMT and graph-to-text
To reproduce the results, Please download data from here and then put the three folders under data/webnlg.
To train the neural planner, run
sh planning.sh
To train & test the PlanEnc model, run
sh pipeline_PlanEnc.sh
To train & test the DualEnc model, run
sh pipeline_DualEnc.sh
###Citation
@inproceedings{zhao-etal-2020-bridging,
title = "Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation",
author = "Zhao, Chao and Walker, Marilyn and Chaturvedi, Snigdha",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.224",
pages = "2481--2491",
}