Building a Question Answering System for the Manufacturing Domain

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 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.