MDKB-Bot: A Practical Framework for Multi-Domain Task-Oriented Dialogue System 后印本

摘要: One of the major challenges to build a task-oriented dialogue system is that dialogue state transition frequently happens between multiple domains such as booking hotels or restaurants. Recently, the encoder#2;decoder model based on the end-to-end neural network has become an attractive approach to meet this challenge. However, it usually requires a sufficiently large amount of training data and it is not flexible to handle dialogue state transition. This paper addresses these problems by proposing a simple but practical framework called Multi-Domain KB-BOT (MDKB-BOT), which leverages both neural networks and rule#2;based strategy in natural language understanding (NLU) and dialogue management (DM). Experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign show that MDKB#2;BOT achieves competitive performance on several evaluation metrics, including task completion rate and user satisfaction.