  In this work, we explore the application of PLATO-2 on various dialogue
systems, including open-domain conversation, knowledge grounded dialogue, and
task-oriented conversation. PLATO-2 is initially designed as an open-domain
chatbot, trained via two-stage curriculum learning. In the first stage, a
coarse-grained response generation model is learned to fit the simplified
one-to-one mapping relationship. This model is applied to the task-oriented
conversation, given that the semantic mappings tend to be deterministic in task
completion. In the second stage, another fine-grained generation model and an
evaluation model are further learned for diverse response generation and
coherence estimation, respectively. With superior capability on capturing
one-to-many mapping, such models are suitable for the open-domain conversation
and knowledge grounded dialogue. For the comprehensive evaluation of PLATO-2,
we have participated in multiple tasks of DSTC9, including interactive
evaluation of open-domain conversation (Track3-task2), static evaluation of
knowledge grounded dialogue (Track3-task1), and end-to-end task-oriented
conversation (Track2-task1). PLATO-2 has obtained the 1st place in all three
tasks, verifying its effectiveness as a unified framework for various dialogue
systems.
