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        <title>Graph Pre-training for AMR Parsing and Generation</title>
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    <abstract>Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation, respectively. However, PLMs are typically pre-trained on textual data, thus are sub-optimal for modeling structural knowledge. To this end, we investigate graph self-supervised training to improve the structure awareness of PLMs over AMR graphs. In particular, we introduce two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-training. We further design a unified framework to bridge the gap between pre-training and fine-tuning tasks. Experiments on both AMR parsing and AMR-to-text generation show the superiority of our model. To our knowledge, we are the first to consider pre-training on semantic graphs.</abstract>
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%0 Conference Proceedings
%T Graph Pre-training for AMR Parsing and Generation
%A Bai, Xuefeng
%A Chen, Yulong
%A Zhang, Yue
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F bai-etal-2022-graph
%X Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure. Recently, pre-trained language models (PLMs) have advanced tasks of AMR parsing and AMR-to-text generation, respectively. However, PLMs are typically pre-trained on textual data, thus are sub-optimal for modeling structural knowledge. To this end, we investigate graph self-supervised training to improve the structure awareness of PLMs over AMR graphs. In particular, we introduce two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-training. We further design a unified framework to bridge the gap between pre-training and fine-tuning tasks. Experiments on both AMR parsing and AMR-to-text generation show the superiority of our model. To our knowledge, we are the first to consider pre-training on semantic graphs.
%R 10.18653/v1/2022.acl-long.415
%U https://aclanthology.org/2022.acl-long.415
%U https://doi.org/10.18653/v1/2022.acl-long.415
%P 6001-6015

Markdown (Informal)

[Graph Pre-training for AMR Parsing and Generation](https://aclanthology.org/2022.acl-long.415) (Bai et al., ACL 2022)

• Graph Pre-training for AMR Parsing and Generation (Bai et al., ACL 2022)

ACL

• Xuefeng Bai, Yulong Chen, and Yue Zhang. 2022. Graph Pre-training for AMR Parsing and Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6001–6015, Dublin, Ireland. Association for Computational Linguistics.