A Selection Support System for Enterprise Resource Planning Package Components using Ensembles of Multiple Models with Round-trip Translation

DOI Web Site 被引用文献1件 参考文献28件 オープンアクセス
  • Ideuchi Masao
    FUJITSU LIMITED National Institute of Information and Communications Technology Nara Institute of Science and Technology
  • Sakamoto Yohei
    Ridgelinez Limited
  • Oida Yoshiaki
    FUJITSU LIMITED The University of Tokyo
  • Okada Isaac
    FUJITSU LIMITED The University of Tokyo Senshu University
  • Higashiyama Shohei
    National Institute of Information and Communications Technology Nara Institute of Science and Technology
  • Utiyama Masao
    National Institute of Information and Communications Technology
  • Sumita Eiichiro
    National Institute of Information and Communications Technology
  • Watanabe Taro
    Nara Institute of Science and Technology

書誌事項

公開日
2021
DOI
  • 10.5715/jnlp.28.1270
公開者
一般社団法人　言語処理学会

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説明

<p>An enterprise resource planning (ERP) package consists of software to support day-to-day business activities and contains multiple components. System engineers combine the most appropriate software components for system integration using ERP packages. Because component selection is a very difficult task, even for experienced system engineers, there is a demand for machine-learning-based systems that support appropriate component selection by reading the text of requirement specifications and predicting suitable components. However, sufficient prediction accuracy has not been achieved thus far as a result of the sparsity and diversity of training data, which consist of specification texts paired with their corresponding components. We implemented round-trip translation at both training and testing times to alleviate the sparsity and diversity problems, adopted pre-trained models to exploit the similarity of text data, and utilized an ensemble of diverse models to take advantage of models for both the original and round-trip translated data. Through experiments with actual project data from ERP system integration, we confirmed that round-trip translation alleviates the problems mentioned above and improves prediction accuracy. As a result, our method achieved sufficient accuracy for practical use. </p>

収録刊行物

  • 自然言語処理

    自然言語処理 28 (4), 1270-1298, 2021

    一般社団法人　言語処理学会

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