工业生产设备故障领域问答系统的意图识别
Intent Detection of Question Answering System in the Field of Industrial Production Equipment Failure
王雨萱 1万卫兵 1程锋1
作者信息
- 1. 上海工程技术大学电子电气工程学院,上海 201620
- 折叠
摘要
为了解决工业生产设备故障领域的问答系统缺乏标注数据、意图识别槽位填充性能不足的问题,提出了一种基于Transformer 的多层双向自注意编码器(bidirectional encoder representations from transformers,BERT)的联合模型.利用 BERT 进行文本序列编码,并通过双向长短时记忆网络(bidirectional long short-term memory,Bi-LSTM)捕捉文本上下文语义关系.通过最大池化和致密层提取关键信息,同时使用条件随机场(conditional random field,CRF)增强模型泛化能力.构建了工业领域设备故障问答语料库,并提出了针对该领域的模型部署框架.在ATIS等公共数据集上进行实验,相对于基线模型,本文模型在句子级准确率、F1和意图识别准确率上,分别提高4.4、2.1和0.5个百分点.研究结果有效提升了问答系统性能,为缺乏工业生产数据的问答系统领域提供了数据集和部署框架.
Abstract
To address the lack of annotated data and insufficient performance in intent detection and slot filling in the domain of in-dustrial equipment failure,a joint model based on BERT was proposed.BERT was utilized for text sequence encoding,while a bidirec-tional long short-term memory(Bi-LSTM)network was employed to capture the semantic relationships within the context.Max pooling and dense layers were used to extract key information,and a conditional random field(CRF)was incorporated to enhance the model's generalization capability.A question-and-answer corpus specifically tailored to the industrial domain of equipment failure was construc-ted,and a deployment framework for this domain was proposed.Experimental evaluations conducted on public datasets such as ATIS demonstrated that the proposed model outperforms baseline models by improving sentence-level accuracy,F1 score,and intent detection accuracy by 4.4％,2.1％,and 0.5％respectively.This research effectively enhances the performance of question-and-answer systems and provides a dataset and deployment framework for the field of industrial equipment failure,which lacks sufficient real-world data.
关键词
意图识别/槽位填充/工业制造领域/问答系统Key words
intent detection/slot filling/industrial manufacturing domain/question-answering system引用本文复制引用
基金项目
科技创新2030-"新一代人工智能"重大项目(2020AAA0109300)
出版年
2024
NETL
NSTL
万方数据