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.- Anthology ID:
- 2023.findings-eacl.154
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2023
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Andreas Vlachos, Isabelle Augenstein
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2064–2076
- Language:
- URL:
- https://aclanthology.org/2023.findings-eacl.154
- DOI:
- 10.18653/v1/2023.findings-eacl.154
- Cite (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.
- Cite (Informal):
- Towards End-to-End Open Conversational Machine Reading (Zhou et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2023.findings-eacl.154.pdf