Research and Application of Multi-Round Dialogue Intent Recognition Method

In the existing dialogue system, there are numerous sentences in non-standardized verbal expression form, which usually is brief and vague. It is a challenging task to identify the intentions through the analysis of these sentences. Considering that the supervised learning approach is the mainstream on multi-intention recognition, an amount of public labeled multi-intention dialogue data is necessary. However, labeling work is costly and time-consuming. In this paper, we put forward a multi-label classification method based on existing mainstream classification algorithms and used for dialogue-level multi-intention recognition to reduce the cost of labeling work. We publish the Chinese Multi-Intention Dialogue (CMID-Transportation) dataset of transportation customer service, which is collected by us in an actual production project. We conduct a series of experiments on the CMID-Transportation corpus by using the mainstream classification algorithms and then produce the basic benchmark performance. We find that BERT achieves the best results. We hope that the CMID-Transportation dataset can promote the research and development of intent recognition tasks in multiple rounds of dialogue.