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        <title>Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders</title>
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        <namePart type="given">Zixia</namePart>
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        <namePart type="given">Youmi</namePart>
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        <namePart type="given">Kewei</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>
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    <abstract>Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations. We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence. Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph. Our model is arc-factored and therefore parsing and learning are both tractable. Experiments show our model achieves significant and consistent improvement over the supervised baseline.</abstract>
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%0 Conference Proceedings
%T Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders
%A Jia, Zixia
%A Ma, Youmi
%A Cai, Jiong
%A Tu, Kewei
%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 jia-etal-2020-semi
%X Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations. We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence. Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph. Our model is arc-factored and therefore parsing and learning are both tractable. Experiments show our model achieves significant and consistent improvement over the supervised baseline.
%R 10.18653/v1/2020.acl-main.607
%U https://aclanthology.org/2020.acl-main.607
%U https://doi.org/10.18653/v1/2020.acl-main.607
%P 6795-6805

Markdown (Informal)

[Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders](https://aclanthology.org/2020.acl-main.607) (Jia et al., ACL 2020)

• Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders (Jia et al., ACL 2020)

ACL

• Zixia Jia, Youmi Ma, Jiong Cai, and Kewei Tu. 2020. Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6795–6805, Online. Association for Computational Linguistics.