In this paper, we propose an end-to-end Dynamic Query Memory Network (DQMemNN) with a delexicalization mechanism for task-oriented dialog systems. The added dynamic component enables memory networks to capture the dialog’s sequential dependencies by using a context-based query. Besides, the delexicalization mechanism reduces learning complexity and it alleviates the out-of-vocabulary entity problems. Experiments show that DQMemNN outperforms original end-to-end memory network models on bAbI full-dialog task by 3.1 % per-response and 39.3% per-dialog accuracy. In addition, the proposed framework achieves a promising average per-response accuracy of 99.7% and per-dialog accuracy of 97.8% without hand-crafted rules and features.
Recommended citation: Wu, C. S., Madotto, A., Winata, G. I., & Fung, P. (2018, April). End-to-end dynamic query memory network for entity-value independent task-oriented dialog. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6154-6158). IEEE.