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GRAM: Grammar-Based Refined-Label Representing Mechanism in the Hierarchical Semantic Parsing Task

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Natural Language Processing and Information Systems (NLDB 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13913))

Abstract

In this study, we proposed an efficient method to improve the performance of the hierarchical semantic parsing task by strengthening the meaning representation of the label candidate set via inductive grammar. In particular, grammar was first synthesized from the logical representations of training annotated data. Then, the model utilizes it as additional structured information for all expression label predictions. The grammar was also used to prevent unpromising directions in the semantic parsing process dynamically. The experimental results on the three well-known semantic parsing datasets, TOP, TOPv2 (low-resource settings), and ATIS, showed that our proposed method work effectiveness, which achieved new state-of-the-art (SOTA) results on TOP and TOPv2 datasets, and competitive results on the ATIS dataset.

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Notes

  1. 1.

    Our source code and the converted version of the ATIS dataset are publicly available at https://github.com/truongdo619/GRAM.

  2. 2.

    The values resulting in the best performance are bold.

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Acknowledgments

This work is supported by AOARD grant FA23862214039.

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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Dinh-Truong Do, Minh-Phuong Nguyen & Le-Minh Nguyen

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  1. Dinh-Truong Do
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  2. Minh-Phuong Nguyen
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Correspondence to Dinh-Truong Do .

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Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Derby, Derby, UK

    Farid Meziane

  3. Oakland University, Rochester, NY, USA

    Vijayan Sugumaran

  4. University of Derby, Derby, UK

    Warren Manning

  5. University of Derby, Derby, UK

    Stephan Reiff-Marganiec

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Do, DT., Nguyen, MP., Nguyen, LM. (2023). GRAM: Grammar-Based Refined-Label Representing Mechanism in the Hierarchical Semantic Parsing Task. In: Métais, E., Meziane, F., Sugumaran, V., Manning, W., Reiff-Marganiec, S. (eds) Natural Language Processing and Information Systems. NLDB 2023. Lecture Notes in Computer Science, vol 13913. Springer, Cham. https://doi.org/10.1007/978-3-031-35320-8_24

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  • DOI: https://doi.org/10.1007/978-3-031-35320-8_24

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