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        <title>DRTS Parsing with Structure-Aware Encoding and Decoding</title>
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        <namePart type="given">Qiankun</namePart>
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            <namePart type="given">Dan</namePart>
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    <abstract>Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a neural sequence-to-sequence model, treating the tree construction as an incremental sequence generation problem. Structural information such as input syntax and the intermediate skeleton of the partial output has been ignored in the model, which could be potentially useful for the DRTS parsing. In this work, we propose a structural-aware model at both the encoder and decoder phase to integrate the structural information, where graph attention network (GAT) is exploited for effectively modeling. Experimental results on a benchmark dataset show that our proposed model is effective and can obtain the best performance in the literature.</abstract>
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%0 Conference Proceedings
%T DRTS Parsing with Structure-Aware Encoding and Decoding
%A Fu, Qiankun
%A Zhang, Yue
%A Liu, Jiangming
%A Zhang, Meishan
%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 fu-etal-2020-drts
%X Discourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a neural sequence-to-sequence model, treating the tree construction as an incremental sequence generation problem. Structural information such as input syntax and the intermediate skeleton of the partial output has been ignored in the model, which could be potentially useful for the DRTS parsing. In this work, we propose a structural-aware model at both the encoder and decoder phase to integrate the structural information, where graph attention network (GAT) is exploited for effectively modeling. Experimental results on a benchmark dataset show that our proposed model is effective and can obtain the best performance in the literature.
%R 10.18653/v1/2020.acl-main.609
%U https://aclanthology.org/2020.acl-main.609/
%U https://doi.org/10.18653/v1/2020.acl-main.609
%P 6818-6828

Markdown (Informal)

[DRTS Parsing with Structure-Aware Encoding and Decoding](https://aclanthology.org/2020.acl-main.609/) (Fu et al., ACL 2020)

• DRTS Parsing with Structure-Aware Encoding and Decoding (Fu et al., ACL 2020)

ACL

• Qiankun Fu, Yue Zhang, Jiangming Liu, and Meishan Zhang. 2020. DRTS Parsing with Structure-Aware Encoding and Decoding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6818–6828, Online. Association for Computational Linguistics.