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.
Intent Detection of Question Answering System in the Field of Industrial Production Equipment Failure
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.
intent detectionslot fillingindustrial manufacturing domainquestion-answering system
NETL
NSTL
万方数据
