Towards End-to-End Open Conversational Machine Reading
Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, Hai Yan Zhao · 2023
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 ShARC and 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-theart results.Further ablation studies support that our framework can generalize to different backbone models.