Adapting Learned Sparse Retrieval for Long Documents
This work addresses a domain-specific problem for information retrieval researchers by improving LSR performance on long documents, though it is incremental as it adapts existing models.
The paper tackled the problem of adapting learned sparse retrieval (LSR) methods like Splade to handle long documents, finding that proximal scoring is crucial and proposing two adaptations (ExactSDM and SoftSDM) that outperform existing approaches on datasets like MSMARCO Document and TREC Robust04.
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.