  Conversational machine reading (CMR) requires machines to communicate with
humans through multi-turn interactions between two salient dialogue states of
decision making and question generation processes. In open CMR settings, as the
more realistic scenario, the retrieved background knowledge would be noisy,
which results in severe challenges in the information transmission. Existing
studies commonly train independent or pipeline systems for the two subtasks.
However, those methods are trivial by using hard-label decisions to activate
question generation, which eventually hinders the model performance. In this
work, we propose an effective gating strategy by smoothing the two dialogue
states in only one decoder and bridge decision making and question generation
to provide a richer dialogue state reference. Experiments on the OR-ShARC
dataset show the effectiveness of our method, which achieves new
state-of-the-art results.
