A BERT-Based Question Answering System for Optical Fiber Transmission Networks

Abstract

To help operations staff quickly get technical support and decision-making basis for highway fiber-optic transmission networks, this paper plans to develop a BERT-based Question Answering (QA) system. The system first builds a knowledge graph to store related knowledge. Then, it uses the BERT model to identify entities and intents in user questions, matches them in the knowledge graph via template matching, and returns answers. This system offers efficient information query and technical support for highway fiber-optic transmission networks. Experimental results show that BERT model has 10.01%, 6.21%, 3.72% higher F1-score than TextRNN, TextCNN, Fasttext in intent recognition. BERT-BiLSTM-CRF model has 10.01%, 6.21%, 3.72% higher F1-score than LSTM, BiLSTM, BiLSTM-CRF in entity recognition.