  Conversational Machine Reading (CMR) aims at answering questions in a
complicated manner. Machine needs to answer questions through interactions with
users based on given rule document, user scenario and dialogue history, and ask
questions to clarify if necessary. In this paper, we propose a dialogue graph
modeling framework to improve the understanding and reasoning ability of
machine on CMR task. There are three types of graph in total. Specifically,
Discourse Graph is designed to learn explicitly and extract the discourse
relation among rule texts as well as the extra knowledge of scenario;
Decoupling Graph is used for understanding local and contextualized connection
within rule texts. And finally a global graph for fusing the information
together and reply to the user with our final decision being either
``Yes/No/Irrelevant" or to ask a follow-up question to clarify.
