通过话题相关性缩小搜索范围，让对话检索快10倍还不丢精度
Efficient Conversational Search via Topical Locality in Dense Retrieval
- 利用对话中话题的连续性，动态筛选相关文档集群
- 在TREC CAsT数据集上提速最高达10.4倍，性能损失仅4.4%
- 适合需要实时响应的对话式搜索系统
预训练语言模型已被广泛用于学习文档和查询的稠密表示以支持信息检索。尽管以往研究主要关注提升效果与用户满意度，响应时间仍是对话式搜索系统的瓶颈。为此，我们利用对话查询固有的话题局部性——即对话中查询倾向于聚焦于相关主题——通过查询嵌入相似性，动态将搜索空间限制在语义相关的文档簇内，从而在不损害检索质量的前提下降低计算复杂度。我们在TREC CAsT 2019和2020数据集上，使用多种嵌入模型和向量索引进行评估，实现了最高10.4倍的处理速度提升，性能损失仅为4.4%（无损失情况下达4.4X）。结果表明，该系统能高效精确地处理复杂的多轮查询，为实时对话式搜索提供了实用解决方案。
原文摘要 · Abstract (English)
Pre-trained language models have been widely exploited to learn dense representations of documents and queries for information retrieval. While previous efforts have primarily focused on improving effectiveness and user satisfaction, response time remains a critical bottleneck of conversational search systems. To address this, we exploit the topical locality inherent in conversational queries, i.e., the tendency of queries within a conversation to focus on related topics. By leveraging query embedding similarities, we dynamically restrict the search space to semantically relevant document clusters, reducing computational complexity without compromising retrieval quality. We evaluate our approach on the TREC CAsT 2019 and 2020 datasets using multiple embedding models and vector indexes, achieving improvements in processing speed of up to 10.4X with little loss in performance (4.4X without any loss). Our results show that the proposed system effectively handles complex, multiturn queries with high precision and efficiency, offering a practical solution for real-time conversational search.
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