Building a Question Answering System for the Manufacturing Domain
Liu Xingguang, Cheng Zhenbo, Shen Zhengyuan, Zhang Haoxin, Meng, Hangcheng, Xu Xuesong, Xiao Gang

TL;DR
This paper presents a natural language processing-based question answering system tailored for the manufacturing domain, specifically aiding pressure vessel design by generating questions from standards and improving answer accuracy.
Contribution
It introduces a question generation method from declarative sentences and an interactive BiLSTM-based attention model to enhance technical question answering accuracy.
Findings
Improved accuracy in technical question answering.
Effective question generation from declarative sentences.
Validated performance on public and technical datasets.
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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Software Engineering Techniques and Practices
