Towards End-to-End Open Conversational Machine Reading

• URL: http://arxiv.org/abs/2210.07113v2
• Date: Fri, 25 Oct 2024 04:11:17 GMT
• Title: Towards End-to-End Open Conversational Machine Reading
• Authors: Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, Hai Zhao,
• Abstract summary: 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. We 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.
• Score: 57.18251784418258
• License:
• Abstract: 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-the-art results. Further ablation studies support that our framework can generalize to different backbone models.

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