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In recent decades, many researchers pay a lot of attention on generating informative responses in end-to-end neural dialogue systems. In order to output the responses with knowledge and fact, many works leverage external knowledge to guide the process of response generation. However, human dialogue is not a simple sequence to sequence task but a process heavily relying on their background knowledge about the topic. Thus, the key of generating informative responses is leveraging the appropriatedoi:10.1109/access.2020.2993924 fatcat:iqzjcl7erjbxrnyrx43kxfnjzy