Intelligent question answering over industrial databases is a challenging task due to the multicolumn context and complex questions. The existing methods need to be improved in terms of SQL generation accuracy. In this paper, we propose a question‐aware few‐shot Text‐to‐SQL approach based on the SDCUP pretrained model. Specifically, an attention‐based filtering approach is proposed to reduce the redundant information from multiple columns in the industrial database scenario. We further propose an operator semantics enhancement method to improve the ability of identifying complex conditions in queries. Experimental results on the industrial benchmarks in the fields of electric energy and structural inspection show that the proposed model outperforms the baseline models across all few‐shot settings.

In order to overcome the problems that the feature representation and classification effect of the existing methods need to be improved in complex contexts, this paper presents a novel few‐shot relation extraction approach via the entity feature enhancement and attention‐based prototypical network. The proposed model uses the pretrained RoBERTa model as the encoder while using the BiLSTM module for directional feature extraction. We further incorporate the entity feature enhancement module to improve the feature representation ability of the model. At last, the attention‐based prototypical network is used to predict relations. The experimental results show that the proposed method not only outperforms the baseline models on the datasets from the bridge inspection and health domains but also achieves competitive results on the FewRel dataset in the general domain.