Building a Question Answering System for the Manufacturing Domain

Abstract

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.

Natural Language Processing in Knowledge-Based Support for Operator Assistance

“…The assembly domain in the assembly environment can be categorized as the closed domain in NLP, which poses a challenge for several reasons. First, to the best of our knowledge, there is limited existing research on the application of NLP techniques in the manufacturing industry, particularly in the area of assembly [19,20]. It means that there is a lack of publicly available documents and specific corpora related to NLP in the assembly.…”