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

By Feng Nie, Jinpeng Wang, Jin-ge Yao, Rong Pan, Chin-Yew Lin. 2018

21 citations recorded by the source corpus.

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.

Subfields: Natural Language Processing

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Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, methodology): “For data processing, OpAtt [136] designs a gating mechanism and a quantization module for the symbolic operation to augment the record table with pre-calculated results.”
Data-to-text Generation with Macro Planning (2021, background): “, 2019) or executing operations such as argmax or minus (Nie et al., 2018) should help alleviate this problem.”