Dialogue manager domain adaptation using Gaussian process reinforcement learning
Table 7: Performance when adding a new agent for the L11 domain to a multi-domain dialogue manager using SCALE training with two partially trained agents for the SFR and SFH domains.
['Performance in L11 domain Training data', 'Performance in L11 domain Reward', 'Performance in L11 domain Success', 'Performance in L11 domain Turns']
It is also important to understand behaviour when a new domain is added alongside a set of existing agents which themselves are not yet fully trained. We are interested in both the performance in the new domain as well as the existing domains. To investigate this, two agents operating in the SFR and SFH domains were pre-trained with 250 dialogues each using SCALE reward distribution mechanism. Performance was then evaluated in the SFR and the new as yet untrained L11 domain. The L11 agent was then trained with 250 dialogues in that domain. Again, the performance was tested in both L11 and SFR. Finally training continued with another 250 dialogues for each of the three domains - SFR, SFH and L11 and the performance in the SFR and L11 domains tested for a final time.