Tae-Hoon Lee  Young-Min Kim  Eunji Jeong  Seon-Ok Na

10.5626/JOK.2021.48.8.878

query intent detection medical advice guidelines for labeling task-oriented dialogue system

In most task-oriented dialogue systems, intent detection and named entity recognition need to precede. This paper deals with the query intent detection to construct a dialogue system for medical advice. We start from the appropriate intent categories for the final goal. We also describe in detail the data collection, training data construction, and the guidelines for the manual annotation. BERT-based classification model has been used for query intent detection. KorBERT, a Korean version of BERT has been also tested for detection. To verify that the DNN-based models outperform the traditional machine learning methods even for a mid-sized dataset, we also tested SVM, which produces a good result in general for such dataset. The F1 scores of SVM, BERT, and KorBERT are 69%, 78%, and 84% respectively. For future work, we will try to increase intent detection performance through dataset improvement.