Open intent detection based on distance loss and decision boundary

In order to solve the problem that the feature distribution is not compact enough due to insufficient processing of feature distribution in open intent detection task, an open intent detection method integrating BERT, distance loss and decision boundary was proposed. Firstly, the context features between texts were captured by BERT model. Then, the learning of sample features was made more compact by distance loss. Finally, decision boundary learning was carried out to achieve the task of open intent detection. The results show that the proposed method has high performance on the public dataset StackOverflow, with the best performance under two different known intent ratio settings, achieving the accuracy of 88.28% and 84.43%, and the F1 values of 87.51% and 87.40%, respectively. The research results complement the future representation reprocessing method for boundary detection,and can provide reference for open intent detection.