让搜索系统自动解释用户模糊查询的真实意图，减少试错。
QUIDS: Query Intent Description for Exploratory Search via Dual Space Modeling
- 用双空间对比学习分离意图相关信号，过滤无关内容
- 在多个指标上超越现有方法，生成描述更准确
- 适合改进探索式搜索的用户体验，提升人机交互
在探索式搜索中，用户常提交模糊查询以了解陌生主题，但搜索系统难以提供明确反馈，导致结果不匹配与反复改写。为解决此问题，本文提出QUIDS，一种基于双空间对比学习的方法，旨在生成用户可读的自然语言意图描述，揭示系统对查询的理解。该方法结合双编码器表示空间与解耦解码器，有效提取意图相关信息并抑制干扰内容。通过意图驱动的困难负样本增强，模型在ROUGE、BERTScore及人工/大模型评估中显著优于当前最优基线。定性分析表明，QUIDS能准确生成探索式搜索中的意图描述。本工作有助于提升用户与搜索系统之间的交互质量。代码已开源：https://github.com/menauwy/QUIDS。
原文摘要 · Abstract (English)
In exploratory search, users often submit vague queries to investigate unfamiliar topics, but receive limited feedback about how the search engine understood their input. This leads to a self-reinforcing cycle of mismatched results and trial-and-error reformulation. To address this, we study the task of generating user-facing natural language query intent descriptions that surface what the system likely inferred the query to mean, based on post-retrieval evidence. We propose QUIDS, a method that leverages dual-space contrastive learning to isolate intent-relevant information while suppressing irrelevant content. QUIDS combines a dual-encoder representation space with a disentangling decoder that works together to produce concise and accurate intent descriptions. Enhanced by intent-driven hard negative sampling, the model significantly outperforms state-of-the-art baselines across ROUGE, BERTScore, and human/LLM evaluations. Our qualitative analysis confirms QUIDS' effectiveness in generating accurate intent descriptions for exploratory search. Our work contributes to improving the interaction between users and search engines by providing feedback to the user in exploratory search settings. Our code is available at https://github.com/menauwy/QUIDS
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