Because of its great potential in understanding and modeling conversations, SEQ2SEQ has been widely applied in different kinds of conversation scenarios including technical supporting, movie discussions, and social entertainment, etc.
when confronting conversations with diverse topics or themes, SEQ2SEQ is usually prone to make generic meaningless responses due to its oversimplified parameterization.