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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs. The losses are then back-propagated to better calibrate our model via multi-task training. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline.</abstract>
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%0 Conference Proceedings
%T Structural Information Preserving for Graph-to-Text Generation
%A Song, Linfeng
%A Wang, Ante
%A Su, Jinsong
%A Zhang, Yue
%A Xu, Kun
%A Ge, Yubin
%A Yu, Dong
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F song-etal-2020-structural
%X The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to tackle this problem by leveraging richer training signals that can guide our model for preserving input information. In particular, we introduce two types of autoencoding losses, each individually focusing on different aspects (a.k.a. views) of input graphs. The losses are then back-propagated to better calibrate our model via multi-task training. Experiments on two benchmarks for graph-to-text generation show the effectiveness of our approach over a state-of-the-art baseline.
%R 10.18653/v1/2020.acl-main.712
%U https://aclanthology.org/2020.acl-main.712
%U https://doi.org/10.18653/v1/2020.acl-main.712
%P 7987-7998

Markdown (Informal)

[Structural Information Preserving for Graph-to-Text Generation](https://aclanthology.org/2020.acl-main.712) (Song et al., ACL 2020)

• Structural Information Preserving for Graph-to-Text Generation (Song et al., ACL 2020)

ACL

• Linfeng Song, Ante Wang, Jinsong Su, Yue Zhang, Kun Xu, Yubin Ge, and Dong Yu. 2020. Structural Information Preserving for Graph-to-Text Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7987–7998, Online. Association for Computational Linguistics.