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A Template-Based Approach for Generating Vietnamese References from Flat MR Dataset in Restaurant Domain

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1306))

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Abstract

In recent years, researchers in natural language generation (NLG) focus on corpus-based systems on specific or across domains. The training data should consist of meaning representations (MRs) paired with Natural Language (NL) references. In the first content of the article, we introduce a Vietnamese Flat MR dataset which is the first Vietnamese dataset for training end-to-end, data-driven NLG systems in restaurant domain. We establish a method of generating references on this dataset. The core of the method are two important stages: (i) sentence planning which determine semantic template of the output text; (ii) surface realization which selecting appropriate Vietnamese phrases to replace the corresponding predicates (slot-value) of the Flat MR in the semantic template. The evaluation results show that the dataset and proposed generating method have contributed well to the development of the NLG research direction.

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Author information

Authors and Affiliations

  1. Sai Gon University, Ho Chi Minh City, Vietnam

    Dang Tuan Nguyen & Trung Tran

Authors
  1. Dang Tuan Nguyen
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  2. Trung Tran
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Correspondence to Dang Tuan Nguyen .

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Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

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Nguyen, D.T., Tran, T. (2020). A Template-Based Approach for Generating Vietnamese References from Flat MR Dataset in Restaurant Domain. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_17

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  • DOI: https://doi.org/10.1007/978-981-33-4370-2_17

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  • Print ISBN: 978-981-33-4369-6

  • Online ISBN: 978-981-33-4370-2

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