A Performance Comparison among Different Amounts of Context on Deep Learning Based Intent Classification Models
Minyoung Jung, Ji-Eun Kim, Jin Yea Jang, Hyedong Jung, Saim Shin · 2020
Human-computer interaction has attracted much attention from researchers and the public. Detecting user intent from a user utterance is a crucial sub-task for human-computer interaction. For task-oriented dialogue systems, correct detection of user intent makes task completion requested by a user possible. Many traditional methods for intent classification suggest rule-based methods which display inherent limitation caused by handcrafted rules. Recently significant research effort has been spent on deep learning based approaches to overcome the limitation of rule-based methods. In this paper, we conduct experiments to compare the performance among different amounts of context on deep learning based intent classification models. We utilize a Long Short-Term Model (LSTM) network and a Recurrent Convolutional Neural Network (RCNN) for intent classification models. We construct our multi-turn dialogue dataset consisting of intent labeled task-oriented dialogues between a user and a dialogue system in Korean. Both LSTM and RCNN based intent classification models illustrate higher accuracies with more amounts of context.