通过伪不相关反馈识别关键维度，提升稠密检索精度
ECLIPSE: Contrastive Dimension Importance Estimation with Pseudo-Irrelevance Feedback for Dense Retrieval
- 用无关文档构建中心点，定位相关文档中的噪声维度
- 在三个域内和一个域外数据集上，mAP最高提升19.50%
- 适合关注提升检索鲁棒性的信息检索研究者
近期信息检索进展利用高维嵌入空间提升文档召回效果。曼达帕聚类假设指出，尽管表示维度很高，但与查询相关的文档实际上位于低维、依赖于查询的流形上。尽管该假设启发了新检索方法，现有技术仍难以有效分离非相关信息与相关信号。本文提出ECLIPSE方法，同时利用相关与非相关文档的信息：通过非相关文档计算中心点作为参考，估计相关文档中存在噪声的维度，从而增强检索性能。在三个域内和一个域外基准测试上，相比基于DIME的基线（分别）实现mAP提升高达19.50%（对应AP）和nDCG@10提升11.42%；相比使用全部维度的基线（分别）实现mAP提升22.35%和nDCG@10提升13.10%。结果为未来基于伪不相关反馈的鲁棒检索系统铺平道路。
原文摘要 · Abstract (English)
Recent advances in Information Retrieval have leveraged high-dimensional embedding spaces to improve the retrieval of relevant documents. Moreover, the Manifold Clustering Hypothesis suggests that despite these high-dimensional representations, documents relevant to a query reside on a lower-dimensional, query-dependent manifold. While this hypothesis has inspired new retrieval methods, existing approaches still face challenges in effectively separating non-relevant information from relevant signals. We propose a novel methodology that addresses these limitations by leveraging information from both relevant and non-relevant documents. Our method, ECLIPSE, computes a centroid based on irrelevant documents as a reference to estimate noisy dimensions present in relevant ones, enhancing retrieval performance. Extensive experiments on three in-domain and one out-of-domain benchmarks demonstrate an average improvement of up to 19.50% (resp. 22.35%) in mAP(AP) and 11.42% (resp. 13.10%) in nDCG@10 w.r.t. the DIME-based baseline (resp. the baseline using all dimensions). Our results pave the way for more robust, pseudo-irrelevance-based retrieval systems in future IR research.
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