Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.
@article{arxiv.1809.02735,
title = {Operations Guided Neural Networks for High Fidelity Data-To-Text Generation},
author = {Feng Nie and Jinpeng Wang and Jin-Ge Yao and Rong Pan and Chin-Yew Lin},
journal= {arXiv preprint arXiv:1809.02735},
year = {2018}
}