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基于BERT模型的自动问答系统的设计与实现

Design and Implementation of an Automatic Question and Answer System Based on BERT Model
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摘要 为解决在线课程学习过程中所遇问题得不到及时解答的情况,设计并实现在线课程自动问答系统。首先收集课程中真实问题作为训练数据集,其次基于BERT模型构建双塔神经网络模型,将问题成对输入模型,以语义相似问题的特征向量尽可能相似为训练目的。训练模型中的参数后,准确率和F1-Score性能指标上的值分别达到0.931和0.918。使用训练好的模型将问题集和学习者提出的问题都转为特征向量,使用Faiss召回问题特征向量集中与学习者的问题最相似的问题,最后返回最相似的问题所对应的答案。系统具有较高的准确性和有效性,能够为在线课程学习提供支持。 To address the situation where problems encountered during online course learning cannot be answered in a timely manner,an automatic question and answer system is designed and implemented.Firstly,real problems in the course are collected as training datasets.Then,a two-tower neural network model is constructed based on the BERT model.It enters the problem in pairs into the model,and the training objective is to train the feature vectors of semantically similar questions to be as similar as possible.After training the parameters of model,the values on accuracy and F1-Score performance indicators reaches 0.931 and 0.918,respectively.This paper uses the trained model to convert the problem sets and the questions proposed by learners into feature vectors,and uses Faiss to recall the most similar questions to the learner's questions in the feature vector sets.Finally,it returns the corresponding answers of the most similar questions.The system has high accuracy and effectiveness,which can provide support for online course learning.
作者 周巧扣 ZHOU Qiaokou(School of Information Engineering,Nanjing Normal University Taizhou College,Taizhou 225300,China)
出处 《现代信息科技》 2024年第20期83-86,共4页 Modern Information Technology
基金 2022年高校哲学社会科学研究一般项目(2022SJYB2365) 2022年江苏省高校“高质量公共课教学改革研究”专项课题(2022JDKT080)。
关键词 自动问答系统 BERT模型 语义相似度 在线学习 automatic question and answer system BERT model semantic similarity online learning
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