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        <namePart type="given">Jinpeng</namePart>
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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.</abstract>
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%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

Markdown (Informal)

[Operation-guided Neural Networks for High Fidelity Data-To-Text Generation](https://aclanthology.org/D18-1422) (Nie et al., EMNLP 2018)

• Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (Nie et al., EMNLP 2018)

ACL

• Feng Nie, Jinpeng Wang, Jin-Ge Yao, Rong Pan, and Chin-Yew Lin. 2018. Operation-guided Neural Networks for High Fidelity Data-To-Text Generation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3879–3889, Brussels, Belgium. Association for Computational Linguistics.