Seen and unseen Chinese intent recognition using automatic probability thresholds

Abstract

Intent recognition is the first part which needs to be accurately recognized in the conversation between ai and the user. Due to the diversity of user intent, it is difficult to manually define all categories of intent in advance during the training phase. The inability to distinguish undefined intent categories can cause the ai to keep replying to the user with seriously wrong answers, which can greatly reduce the user's sense of experience. Therefore, it is very necessary to realize the recognition of seen intention categories and the distinction of unseen intention categories. In this paper, a model with Automatic probability threshold which based on the bert model is used to ensure accurate recognition of seen intentions while distinguishing unseen intentions. Due to the inadequacy of training samples, the automatic probability threshold model in the paper uses text vectors from two bert models containing different dropout parameters as the training set, which can improve accuracy of the model.