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Transferring SLU Models in Novel Domains
When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter whether the data is annotated or not.
Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Also, they depend on either common slots or slot entropy, which are not available when the source and target slots are totally disjoint and no database is available to calculate the slot entropy.
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems
Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient.
Integrating User and Agent Models: A Deep Task-Oriented Dialogue System
Then the built user model is used as a leverage to train the agent model by deep reinforcement learning.
Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-of-vocabulary entities.
Personalizing a Dialogue System with Transfer Reinforcement Learning
One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.
