Building a Question Answering System for the Manufacturing Domain
This is an incremental improvement for manufacturing engineers needing precise answers from standards, but it is domain-specific.
The authors tackled the problem of accurately answering technical questions in pressure vessel design by developing a question answering system using natural language processing, which was tested on public and technical datasets.
The design or simulation analysis of special equipment products must follow the national standards, and hence it may be necessary to repeatedly consult the contents of the standards in the design process. However, it is difficult for the traditional question answering system based on keyword retrieval to give accurate answers to technical questions. Therefore, we use natural language processing techniques to design a question answering system for the decision-making process in pressure vessel design. To solve the problem of insufficient training data for the technology question answering system, we propose a method to generate questions according to a declarative sentence from several different dimensions so that multiple question-answer pairs can be obtained from a declarative sentence. In addition, we designed an interactive attention model based on a bidirectional long short-term memory (BiLSTM) network to improve the performance of the similarity comparison of two question sentences. Finally, the performance of the question answering system was tested on public and technical domain datasets.