UnifieR: A Unified Retriever for Large-Scale Retrieval
This work addresses retrieval efficiency and effectiveness for applications like search engines, though it is incremental as it builds on existing paradigms.
The paper tackles the problem of large-scale retrieval by proposing UnifieR, a unified model that combines dense-vector and lexicon-based retrieval paradigms, achieving improved retrieval quality and verified transferability on benchmarks like BEIR.
Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semantic encoding space. According to the encoding space, recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. These two paradigms unveil the PLMs' representation capability in different granularities, i.e., global sequence-level compression and local word-level contexts, respectively. Inspired by their complementary global-local contextualization and distinct representing views, we propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability. Experiments on passage retrieval benchmarks verify its effectiveness in both paradigms. A uni-retrieval scheme is further presented with even better retrieval quality. We lastly evaluate the model on BEIR benchmark to verify its transferability.