Data Augmentation for building QA Systems based on Object Models with Star Schema

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Enterprises produce massive amounts of data every day. Data records generated in various formats are usually classified into structured, semi-structured, and unstructured data [1]. Many transactional data records are crucial and need to be exchanged between systems, thus data conversion becomes necessary and even tedious. Moreover, decision-makers always make "ad hoc"requests that require searching within large volumes of data. Therefore, an intelligent system is needed to respond rapidly to the demands of modern enterprises. In this paper, we purpose a novel method to build a question-answering (QA) system from a transactional system. We use object models that are translated from a star schema to represent the transactional system, such that it can generate questions (Natural Language, NL) and answers (SQL statements) as the training data. Then, we use an end-to-end (E2E) neural network to train a QA system with the generated data. Our experiments show that the Long Short-Term Memory (LSTM) network with a 0.95 BLEU value is more accurate than the Gated Recurrent Unit (GRU) network with a 0.90 BLEU value. Consequently, the proposed method can automatically generate training data from object models, and the trained artificial intelligence (AI) model can further become a QA system for us to ask questions directly.

Original languageEnglish
Title of host publication2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications, ICPECA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages244-249
Number of pages6
ISBN (Electronic)9781665472784
DOIs
Publication statusPublished - 2023
Event3rd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2023 - Shenyang, China
Duration: 29 Jan 202331 Jan 2023

Publication series

Name2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications, ICPECA 2023

Conference

Conference3rd IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2023
Country/TerritoryChina
CityShenyang
Period29/01/2331/01/23

Keywords

  • QA System
  • data augmentation
  • data transformation
  • natural language query
  • star schema

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