Sparse, Dense, and Attentional Representations for Text Retrieval
This addresses retrieval efficiency and accuracy challenges for large-scale text search systems, representing an incremental improvement over existing methods.
The paper investigated the capacity limitations of dual encoder architectures for text retrieval, particularly for long documents, and proposed a hybrid neural model combining efficient dual encoders with expressive attentional mechanisms and sparse-dense hybrids. The proposed models outperformed strong alternatives in large-scale retrieval.
Dual encoders perform retrieval by encoding documents and queries into dense lowdimensional vectors, scoring each document by its inner product with the query. We investigate the capacity of this architecture relative to sparse bag-of-words models and attentional neural networks. Using both theoretical and empirical analysis, we establish connections between the encoding dimension, the margin between gold and lower-ranked documents, and the document length, suggesting limitations in the capacity of fixed-length encodings to support precise retrieval of long documents. Building on these insights, we propose a simple neural model that combines the efficiency of dual encoders with some of the expressiveness of more costly attentional architectures, and explore sparse-dense hybrids to capitalize on the precision of sparse retrieval. These models outperform strong alternatives in large-scale retrieval.