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        <title>Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
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    <abstract>Neural QCFG is a grammar-based sequence-to-sequence model with strong inductive biases on hierarchical structures. It excels in interpretability and generalization but suffers from expensive inference. In this paper, we study two low-rank variants of Neural QCFG for faster inference with different trade-offs between efficiency and expressiveness. Furthermore, utilizing the symbolic interface provided by the grammar, we introduce two soft constraints over tree hierarchy and source coverage. We experiment with various datasets and find that our models outperform vanilla Neural QCFG in most settings.</abstract>
    <identifier type="citekey">lou-tu-2023-improving</identifier>
    <identifier type="doi">10.18653/v1/2023.acl-short.163</identifier>
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%0 Conference Proceedings
%T Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints
%A Lou, Chao
%A Tu, Kewei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lou-tu-2023-improving
%X Neural QCFG is a grammar-based sequence-to-sequence model with strong inductive biases on hierarchical structures. It excels in interpretability and generalization but suffers from expensive inference. In this paper, we study two low-rank variants of Neural QCFG for faster inference with different trade-offs between efficiency and expressiveness. Furthermore, utilizing the symbolic interface provided by the grammar, we introduce two soft constraints over tree hierarchy and source coverage. We experiment with various datasets and find that our models outperform vanilla Neural QCFG in most settings.
%R 10.18653/v1/2023.acl-short.163
%U https://aclanthology.org/2023.acl-short.163
%U https://doi.org/10.18653/v1/2023.acl-short.163
%P 1918-1929

Markdown (Informal)

[Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints](https://aclanthology.org/2023.acl-short.163) (Lou & Tu, ACL 2023)

• Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints (Lou & Tu, ACL 2023)

ACL

• Chao Lou and Kewei Tu. 2023. Improving Grammar-based Sequence-to-Sequence Modeling with Decomposition and Constraints. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1918–1929, Toronto, Canada. Association for Computational Linguistics.