DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
Authors: name: Thong Nguyen, name: Shubham Chatterjee, name: Sean MacAvaney, name: Iain Mackie, name: Jeff Dalton, name: Andrew Yates

Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained
transformers, which often split entities into nonsensical fragments. Splitting
entities can reduce retrieval accuracy and limits the model's ability to
incorporate up-to-date world knowledge not included in the training data. In
this work, we enhance the LSR vocabulary with Wikipedia concepts and entities,
enabling the model to resolve ambiguities more effectively and stay current
with evolving knowledge. Central to our approach is a Dynamic Vocabulary (DyVo)
head, which leverages existing entity embeddings and an entity retrieval
component that identifies entities relevant to a query or document. We use the
DyVo head to generate entity weights, which are then merged with word piece
weights to create joint representations for efficient indexing and retrieval
using an inverted index. In experiments across three entity-rich document
ranking datasets, the resulting DyVo model substantially outperforms
state-of-the-art baselines.