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        <title>StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing</title>
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    <abstract>Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. StructVAE models latent MRs not observed in the unlabeled data as tree-structured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, StructVAE outperforms strong supervised models.</abstract>
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%0 Conference Proceedings
%T StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
%A Yin, Pengcheng
%A Zhou, Chunting
%A He, Junxian
%A Neubig, Graham
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F yin-etal-2018-structvae
%X Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures. Annotating NL utterances with their corresponding MRs is expensive and time-consuming, and thus the limited availability of labeled data often becomes the bottleneck of data-driven, supervised models. We introduce StructVAE, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances. StructVAE models latent MRs not observed in the unlabeled data as tree-structured latent variables. Experiments on semantic parsing on the ATIS domain and Python code generation show that with extra unlabeled data, StructVAE outperforms strong supervised models.
%R 10.18653/v1/P18-1070
%U https://aclanthology.org/P18-1070
%U https://doi.org/10.18653/v1/P18-1070
%P 754-765

Markdown (Informal)

[StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing](https://aclanthology.org/P18-1070) (Yin et al., ACL 2018)

• StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing (Yin et al., ACL 2018)

ACL

• Pengcheng Yin, Chunting Zhou, Junxian He, and Graham Neubig. 2018. StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 754–765, Melbourne, Australia. Association for Computational Linguistics.