However, the language model of the source and target domains may differ, we therefore finetune the separator of DNPG so that it can identify the granularity of the sentence in the target domain more accurately.
to adapt the separator P sep (Z|X) to the target domain, we employ a reinforcement learning (RL) approach by maxi-mizing the accumulative reward: We define the reward functions based on the principle that the source and target domain share the similar sentence templates.