  Task-Oriented Dialogue (TOD) systems are designed to carry out specific tasks
by tracking dialogue states and generating appropriate responses to help users
achieve defined goals. Recently, end-to-end dialogue models pre-trained based
on large datasets have shown promising performance in the conversational
system. However, they share the same parameters to train tasks of the dialogue
system (NLU, DST, NLG), so debugging each task is challenging. Also, they
require a lot of effort to fine-tune large parameters to create a task-oriented
chatbot, making it difficult for non-experts to handle. Therefore, we intend to
train relatively lightweight and fast models compared to PLM. In this paper, we
propose an End-to-end TOD system with Task-Optimized Adapters which learn
independently per task, adding only small number of parameters after fixed
layers of pre-trained network. We also enhance the performance of the DST and
NLG modules through reinforcement learning, overcoming the learning curve that
has lacked at the adapter learning and enabling the natural and consistent
response generation that is appropriate for the goal. Our method is a
model-agnostic approach and does not require prompt-tuning as only input data
without a prompt. As results of the experiment, our method shows competitive
performance on the MultiWOZ benchmark compared to the existing end-to-end
models. In particular, we attain state-of-the-art performance on the DST task
of 2.2 dataset.
