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.

本文介绍了一种基于完全端到端的方式将开放检索对话式机器阅读（OR-CMR）建模为一个统一的文本到文本任务，并在 OR-ShARC 数据集上展示了其在两个子任务上的显著有效性和创新的最先进结果。