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ICR: Iterative Clarification and Rewriting for Conversational Search

Created by
  • Haebom

Author

Zhiyu Cao, Peifeng Li, Qiaoming Zhu

Outline

This paper highlights that most existing conversational query rewriting research relies on an end-to-end approach, which struggles to simultaneously identify and rewrite multiple ambiguous expressions within a query. To address this, we propose a novel framework, Iterative Clarification and Rewriting (ICR), which performs iterative rewriting centered on query disambiguation. ICR alternates between generating disambiguation questions and rewriting them. Experimental results demonstrate that ICR consistently improves retrieval performance through this iterative process on two representative datasets, achieving state-of-the-art performance.

Takeaways, Limitations

Takeaways:
A novel approach to overcome the limitations of end-to-end methods in conversational query rewriting is presented.
Resolve ambiguity issues and improve search performance by using query disambiguation questions.
Demonstrated potential for continuous performance improvement through an iterative clarification-rewrite process.
Achieving state-of-the-art performance on two representative datasets.
Limitations:
Further research is needed to determine the generalizability of the proposed ICR framework.
Further performance evaluations are needed for various types of queries and datasets.
A more in-depth analysis of the quality and effectiveness of clarifying questions is needed.
Evaluation of applicability and user experience in real-world usage environments is required.
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