Linear Function Relation Identification Based on BERT and Bi-LSTM

Problem solving technology is a hot research issue in intelligent education. Linear function scenario problem is one of the important types of problems. This paper presents a linear function relation identification algorithm for solving linear function problems. Firstly, the problem text was transformed into semantic vectors through the BERT model. Secondly, a linear function relation candidate set is created and a Bi-LSTM based identification model is used to select the correct set of linear relations among candidates. Finally, a two-stage solving method is used to obtain the implicit and explicit relations from the correct set of linear relations to get the result. The experiment was tested on 486 linear function scenario problems. The result shows our algorithm achieved 86.1% accuracy in finding the correct set of linear relations and 59.4% accuracy in solving linear function scenario problems.