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        <title>Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation</title>
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    <name type="personal">
        <namePart type="given">Chao</namePart>
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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>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>Generating sequential natural language descriptions from graph-structured data (e.g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text. Hence, popular sequence-to-sequence models, which require serialized input, are not a natural fit for this task. Graph neural networks, on the other hand, can better encode the input graph but broaden the structural gap between the encoder and decoder, making faithful generation difficult. To narrow this gap, we propose DualEnc, a dual encoding model that can not only incorporate the graph structure, but can also cater to the linear structure of the output text. Empirical comparisons with strong single-encoder baselines demonstrate that dual encoding can significantly improve the quality of the generated text.</abstract>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation
%A Zhao, Chao
%A Walker, Marilyn
%A Chaturvedi, Snigdha
%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 zhao-etal-2020-bridging
%X Generating sequential natural language descriptions from graph-structured data (e.g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text. Hence, popular sequence-to-sequence models, which require serialized input, are not a natural fit for this task. Graph neural networks, on the other hand, can better encode the input graph but broaden the structural gap between the encoder and decoder, making faithful generation difficult. To narrow this gap, we propose DualEnc, a dual encoding model that can not only incorporate the graph structure, but can also cater to the linear structure of the output text. Empirical comparisons with strong single-encoder baselines demonstrate that dual encoding can significantly improve the quality of the generated text.
%R 10.18653/v1/2020.acl-main.224
%U https://aclanthology.org/2020.acl-main.224/
%U https://doi.org/10.18653/v1/2020.acl-main.224
%P 2481-2491

Markdown (Informal)

[Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation](https://aclanthology.org/2020.acl-main.224/) (Zhao et al., ACL 2020)

• Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation (Zhao et al., ACL 2020)

ACL

• Chao Zhao, Marilyn Walker, and Snigdha Chaturvedi. 2020. Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2481–2491, Online. Association for Computational Linguistics.