To address this challenge, we propose a hybrid imitation and reinforcement learning method, with which a dialogue agent can effectively learn from its interaction with users by learning from human teaching and feedback.
#4 best model for Dialogue State Tracking on Second dialogue state tracking challenge
We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge.
Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems.
#2 best model for Dialogue State Tracking on Second dialogue state tracking challenge
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.
Existing solutions to task-oriented dialogue systems follow pipeline designs which introduces architectural complexity and fragility.
Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats.
Teaching machines to accomplish tasks by conversing naturally with humans is challenging.