Learned sparse retrieval (LSR) is a family of neural retrieval methods that transform queries and documents into sparse weight vectors aligned with a vocabulary. While LSR approaches like Splade work well for short passages, it is unclear how well they handle longer documents. We investigate existing aggregation approaches for adapting LSR to longer documents and find that proximal scoring is crucial for LSR to handle long documents. To leverage this property, we proposed two adaptations of the Sequential Dependence Model (SDM) to LSR: ExactSDM and SoftSDM. ExactSDM assumes only exact query term dependence, while SoftSDM uses potential functions that model the dependence of query terms and their expansion terms (i.e., terms identified using a transformer's masked language modeling head). Experiments on the MSMARCO Document and TREC Robust04 datasets demonstrate that both ExactSDM and SoftSDM outperform existing LSR aggregation approaches for different document length constraints. Surprisingly, SoftSDM does not provide any performance benefits over ExactSDM. This suggests that soft proximity matching is not necessary for modeling term dependence in LSR. Overall, this study provides insights into handling long documents with LSR, proposing adaptations that improve its performance.

翻译：学习型稀疏检索（LSR）是一类将查询和文档转换为与词汇表对齐的稀疏权重向量的神经检索方法。尽管诸如Splade等LSR方法在短文本片段上表现良好，但其对长文档的处理能力尚不明确。本文研究了将LSR适配至长文档的现有聚合方法，发现邻近性评分（Proximal Scoring）对LSR处理长文档至关重要。基于此特性，我们提出了序列依赖模型（SDM）的两种LSR适配方案：ExactSDM与SoftSDM。ExactSDM仅假设精确查询词项的依赖关系，而SoftSDM采用潜在函数建模查询词项及其扩展词项（即通过Transformer掩码语言建模头识别的词项）之间的依赖关系。在MSMARCO文档数据集和TREC Robust04数据集上的实验表明，在不同文档长度约束下，ExactSDM和SoftSDM均优于现有LSR聚合方法。令人意外的是，SoftSDM相较于ExactSDM并未带来性能提升，这表明软邻近匹配对LSR中词项依赖建模并非必需。本研究为利用LSR处理长文档提供了深刻见解，并提出了提升其性能的适配方案。