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    <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.</abstract>
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%0 Conference Proceedings
%T Towards End-to-End Open Conversational Machine Reading
%A Zhou, Sizhe
%A Ouyang, Siru
%A Zhang, Zhuosheng
%A Zhao, Hai
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F zhou-etal-2023-towards
%X 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.
%R 10.18653/v1/2023.findings-eacl.154
%U https://aclanthology.org/2023.findings-eacl.154
%U https://doi.org/10.18653/v1/2023.findings-eacl.154
%P 2064-2076

Markdown (Informal)

[Towards End-to-End Open Conversational Machine Reading](https://aclanthology.org/2023.findings-eacl.154) (Zhou et al., Findings 2023)

• Towards End-to-End Open Conversational Machine Reading (Zhou et al., Findings 2023)

ACL

• Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, and Hai Zhao. 2023. Towards End-to-End Open Conversational Machine Reading. In Findings of the Association for Computational Linguistics: EACL 2023, pages 2064–2076, Dubrovnik, Croatia. Association for Computational Linguistics.