  In open-retrieval conversational machine reading (OR-CMR) task, machines are
required to do multi-turn question answering given dialogue history and a
textual knowledge base. Existing works generally utilize two independent
modules to approach this problem's two successive sub-tasks: first with a
hard-label decision making and second with a question generation aided by
various entailment reasoning methods. Such usual cascaded modeling is
vulnerable to error propagation and prevents the two sub-tasks from being
consistently optimized. In this work, we instead model OR-CMR as a unified
text-to-text task in a fully end-to-end style. Experiments on the OR-ShARC
dataset show the effectiveness of our proposed end-to-end framework on both
sub-tasks by a large margin, achieving new state-of-the-art results. Further
ablation studies support that our framework can generalize to different
backbone models.
