Data Augmentation for building QA Systems based on Object Models with Star Schema
Lap Man Hoi, Wei Ke, Sio‐Kei Im · 2023
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.