%0 Conference Proceedings %T Operation-guided Neural Networks for High Fidelity Data-To-Text Generation %A Nie, Feng %A Wang, Jinpeng %A Yao, Jin-Ge %A Pan, Rong %A Lin, Chin-Yew %Y Riloff, Ellen %Y Chiang, David %Y Hockenmaier, Julia %Y Tsujii, Jun’ichi %S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing %D 2018 %8 oct nov %I Association for Computational Linguistics %C Brussels, Belgium %F nie-etal-2018-operation %X 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. %R 10.18653/v1/D18-1422 %U https://aclanthology.org/D18-1422 %U https://doi.org/10.18653/v1/D18-1422 %P 3879-3889