论文标题
通过学习稀疏表示的双重跳过指南
Dual Skipping Guidance for Document Retrieval with Learned Sparse Representations
论文作者
论文摘要
本文提出了一种具有混合评分的双重跳过指导方案，以加速使用稀疏表示的文档检索，同时仍提供良好的相关性。该方案同时使用词汇BM25和学习的神经学期权重来绑定和构成候选文档的排名评分，以跳过和最终排名，并在倒置索引遍历期间保持两个Top-K阈值。本文评估了搜索MS MARCO TREC数据集时所提出的方案的时间效率和排名相关性。
This paper proposes a dual skipping guidance scheme with hybrid scoring to accelerate document retrieval that uses learned sparse representations while still delivering a good relevance. This scheme uses both lexical BM25 and learned neural term weights to bound and compose the rank score of a candidate document separately for skipping and final ranking, and maintains two top-k thresholds during inverted index traversal. This paper evaluates time efficiency and ranking relevance of the proposed scheme in searching MS MARCO TREC datasets.
