4 papers
Dynamic Superblock Pruning for Fast Learned Sparse Retrieval
Parker Carlson, Wentai Xie, Shanxiu He +1
This paper proposes superblock pruning (SP) during top-k online document retrieval for learned sparse representations. SP structures the sparse index as a set of superblocks on a s…
LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval
Yingrui Yang, Parker Carlson, Yifan Qiao +3
This paper studies fast fusion of dense retrieval and sparse lexical retrieval, and proposes a cluster-based selective dense retrieval method called CluSD guided by sparse lexical…
Weighted KL-Divergence for Document Ranking Model Refinement
Yingrui Yang, Yifan Qiao, Shanxiu He +1
Transformer-based retrieval and reranking models for text document search are often refined through knowledge distillation together with contrastive learning. A tight distribution…
Approximate Cluster-Based Sparse Document Retrieval with Segmented Maximum Term Weights
Yifan Qiao, Shanxiu He, Yingrui Yang +2
This paper revisits cluster-based retrieval that partitions the inverted index into multiple groups and skips the index partially at cluster and document levels during online infer…