End-to-End Recurrent Entity Network for Entity-Value Independent Goal-Oriented Dialog Learning

Published in Dialog System Technology Challenges Workshop, DSTC6 (top 2 systems), 2017

Recommended citation: Wu, C. S., Madotto, A., Winata, G., & Fung, P. (2017). End-to-end recurrent entity network for entity-value independent goal-oriented dialog learning. In Dialog System Technology Challenges Workshop, DSTC6. http://workshop.colips.org/dstc6/papers/track1_paper02_wu.pdf

This paper presents an end-to-end solution for the goal-oriented dialog system task in Dialog System Technology Challenges 6 (DSTC6). The challenge consists in learning a dialog policy from a given restaurant booking domain. End-to-end models are required to reason over dialog entities and to track the dialog states. Hence, we introduce a practical entity-value independent framework based on Recurrent Entity Networks. The framework is able to abstract linguistic entity by using a delexicalization mechanism, which improves the original model performance especially in test sets with out-of-vocabulary entities. Recurrent Entity Networks also plays an important role to represent the latent dialog state and the dialog policy. As shown in experiments, our framework can achieve a promising average Precision-1 of 96.56% in all the test sets.

Paper

Recommended citation: Wu, C. S., Madotto, A., Winata, G., & Fung, P. (2017). End-to-end recurrent entity network for entity-value independent goal-oriented dialog learning. In Dialog System Technology Challenges Workshop, DSTC6.