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arXiv · 1809.02735

Operations Guided Neural Networks for High Fidelity Data-To-Text Generation

Feng Nie·Jinpeng Wang·Jin-Ge Yao·Rong Pan·Chin-Yew Lin
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Abstract

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.

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Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong Pan, Chin-Yew Lin. 2018-09-08. Operations Guided Neural Networks for High Fidelity Data-To-Text Generation. https://arxiv.org/abs/1809.02735

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