Operation-guided Neural Networks for High Fidelity Data-To-Text Generation
Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong Pan, Chin-Yew Lin · 2018
Recent neural models for data-to-text generation are mostly based on data-driven end-toend 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 Operationguided 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.