Transfer learning-based query classification for intelligent building information spoken dialogue

Retrieving queried information from building information models (BIM) requires experience in structured query languages and manipulation of BIM software. Artificial Intelligence (AI)-based spoken dialogue systems provide more opportunities for information retrieval from building information models via natural language queries. This research developed a transfer learning-based text classification (TC) method to classify different queries into pre-defined categories for an intelligent building information spoken dialogue system (iBISDS), a virtual assistant that provides information retrieval support for construction project team members. The architecture of the TC neural network (NN) was built based on the pre-trained Robustly Optimized BERT Pretraining Approach (RoBERTa). After the training process, the re-trained and fine-tuned RoBERTa NN achieved a precision of 99.76%, a recall of 99.76%, and an F1 score of 99.76% on the testing dataset. The experimental results indicated that the developed NN algorithm for TC can relatively accurately classify different building information-related queries into pre-defined TC categories.